{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solving the heat equation in PyBaMM\n",
    "\n",
    "In this notebook we create and solve a model for unsteady heat diffusion in 1D, with a spatially dependent heat source term. The notebook is adapted from example 4.1.2 on pg.16 of the online notes found [here](https://faculty.uca.edu/darrigo/Students/M4315/Fall%202005/sep-var.pdf). \n",
    "\n",
    "We consider the heat equation \n",
    "\n",
    "$$T_{t} = kT_{xx} + Q(x), \\quad 0 < x < L, \\quad t > 0,$$\n",
    "\n",
    "along with the boundary and initial conditions, \n",
    "\n",
    "$$u(0, t)=0, \\quad u(L, t)=0, \\quad u(x, 0)=2x-x^2,$$\n",
    "\n",
    "and heat source term \n",
    "\n",
    "$$ Q(x)=1-|x-1|.$$\n",
    "\n",
    "As in the example, we solve the problem on the domain $0 < x < 2$ (i.e. we take $L=2$). We extended the example to include a thermal diffusivity $k$, which we take to be equal to 0.75."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the model\n",
    "As always, we start by importing PyBaMM, along with any other packages we require."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import pybamm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then load up an instance of the `pybamm.BaseModel` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = pybamm.BaseModel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now define the model variables and parameters. Note that we also need to define the spatial variable $x$ here, so that we can write down the spatially dependent source term $Q(x)$. Since we are solving in 1D we have decided to call the domain \"rod\", but we could name it anything we like. Note that in PyBaMM variables and parameters can be given useful and meaningful names, such as \"Temperature\", so that they can be easily referred to later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pybamm.SpatialVariable(\"x\", domain=\"rod\", coord_sys=\"cartesian\")\n",
    "T = pybamm.Variable(\"Temperature\", domain=\"rod\")\n",
    "k = pybamm.Parameter(\"Thermal diffusivity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have defined the variables, we can write down the model equations and add them to the `model.rhs` dictionary. This dictionary stores the right hand sides of any time-dependent differential equations (ordinary or partial). The key is the variable inside the time derivative (in this case $T$). To define the heat source term we use a `pybamm.Function` class. The first argument of the class is the function, and the second argument is the input.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = -k * pybamm.grad(T)  # Heat flux\n",
    "Q = 1 - pybamm.Function(np.abs, x - 1)  # Source term\n",
    "dTdt = -pybamm.div(N) + Q  # The right hand side of the PDE\n",
    "model.rhs = {T: dTdt}  # Add to model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now add the boundary conditions into the `model.boundary_conditions` dictionary. The keys of the dictionary indicate which end of the boundary the condition is applied to (in 1D this can be \"left\" or \"right\"), the entry is then give as a tuple of the value and type. In this example we have homogeneous Dirichlet boundary conditions at both ends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.boundary_conditions = {\n",
    "    T: {\n",
    "        \"left\": (pybamm.Scalar(0), \"Dirichlet\"),\n",
    "        \"right\": (pybamm.Scalar(0), \"Dirichlet\"),\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to add the initial conditions to the `model.initial_conditions` dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.initial_conditions = {T: 2 * x - x**2}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we add any output variables to the `model.variables` dictionary. These variables can be easily accessed after the model has been solved. You can add any variables of interest to this dictionary. Here we have added the temperature, heat flux and heat source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.variables = {\"Temperature\": T, \"Heat flux\": N, \"Heat source\": Q}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the model\n",
    "Now that the model has been constructed we can go ahead and define our geometry and parameter values. We start by defining the geometry for our \"rod\" domain. We need to define the spatial direction(s) in which spatial operators act (such as gradients). In this case it is simply $x$. We then set the minimum and maximum values $x$ can take. In this example we are solving the problem on the domain $0<x<2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "geometry = {\"rod\": {x: {\"min\": pybamm.Scalar(0), \"max\": pybamm.Scalar(2)}}}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to provide the values of any parameters using the `pybamm.ParameterValues` class. This class accepts a dictionary of parameter names and values. Note that the name we provide is the string name of the parameters and not its symbol."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "param = pybamm.ParameterValues({\"Thermal diffusivity\": 0.75})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have defined the geometry and provided the parameters values, we can process the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "param.process_model(model)\n",
    "param.process_geometry(geometry)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we disctretise the spatial operators, we must choose and mesh and a spatial method. Here we choose to use a uniformly spaced 1D mesh with 30 points, and discretise the equations in space using the finite volume method. The information about the mesh is stored in a `pybamm.Mesh` object, wheres the spatial methods are stored in a dictionary which maps domain names to a spatial method. This allows the user to discretise different (sub)domains in a problem using different spatial methods. All of this information goes into a `pybamm.Discretisation` object, which accepts a mesh and a dictionary of spatial methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "submesh_types = {\"rod\": pybamm.Uniform1DSubMesh}\n",
    "var_pts = {x: 30}\n",
    "mesh = pybamm.Mesh(geometry, submesh_types, var_pts)\n",
    "spatial_methods = {\"rod\": pybamm.FiniteVolume()}\n",
    "disc = pybamm.Discretisation(mesh, spatial_methods)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is then processed using the discretisation, turning the spaital operators into matrix-vector multiplications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pybamm.models.base_model.BaseModel at 0x7f39430e6ad0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disc.process_model(model)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the model has been discretised we are ready to solve. We must first choose a solver to use. For this model we choose the Scipy ODE solver, but other solvers are available in PyBaMM (see [here](https://docs.pybamm.org/en/latest/source/api/solvers/index.html)). To solve the model, we use the method `solver.solve` which takes in a model and an array of times at which we would like the solution to be returned. Ths solution is then stored in the `solution` object. The times and states can be accessed with `solver.t` and `solver.y`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "solver = pybamm.ScipySolver()\n",
    "t = np.linspace(0, 1, 100)\n",
    "solution = solver.solve(model, t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After solving, we can process variables using the class `pybamm.ProcessedVariable`. This returns a callable object which can be evaluated at any time and position by means of interpolating the solution. Processed variables provide a convinient way of comparing the solution to solutions from different models, or to exact solutions. Since all of the variables are names with informative strings, the user doesn't need to keep track of where they are stored in the state vector `solution.y`. This is particularly useful in complex models with lots of variables, and is automatically handled by the solution dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-01-24 20:03:41,111 - [WARNING] processed_variable.get_spatial_scale(518): No length scale set for rod. Using default of 1 [m].\n"
     ]
    }
   ],
   "source": [
    "T_out = solution[\"Temperature\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison with the exact solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example admits the exact solution \n",
    "\n",
    "$$T(x,t) = \\sum_{n=1}^{\\infty} \\left(\\frac{4}{kn^2\\pi^2}q_n + \\left( c_n - \\frac{4}{kn^2\\pi^2}q_n\\right) \\exp^{-k\\left(\\frac{n\\pi}{2}\\right)^2t} \\right) \\sin\\left( \\frac{n\\pi x}{2}\\right),$$\n",
    "\n",
    "with \n",
    "\n",
    "$$c_n = \\frac{16}{n^3\\pi^3}\\left(1 - \\cos(n \\pi)\\right), \\quad \\text{and} \\quad q_n = \\frac{8}{n^2\\pi^2} \\sin\\left(\\frac{n\\pi}{2}\\right).$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We construct the exact solution by summing over some large number $N$ of terms in the Fourier series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 100  # number of Fourier modes to sum\n",
    "k_val = param[\n",
    "    \"Thermal diffusivity\"\n",
    "]  # extract value of diffusivity from the parameters dictionary\n",
    "\n",
    "\n",
    "# Fourier coefficients\n",
    "def q(n):\n",
    "    return (8 / (n**2 * np.pi**2)) * np.sin(n * np.pi / 2)\n",
    "\n",
    "\n",
    "def c(n):\n",
    "    return (16 / (n**3 * np.pi**3)) * (1 - np.cos(n * np.pi))\n",
    "\n",
    "\n",
    "def b(n):\n",
    "    return c(n) - 4 * q(n) / (k_val * n**2 * np.pi**2)\n",
    "\n",
    "\n",
    "def T_n(t, n):\n",
    "    return (4 * q(n) / (k_val * n**2 * np.pi**2)) + b(n) * np.exp(\n",
    "        -k_val * (n * np.pi / 2) ** 2 * t\n",
    "    )\n",
    "\n",
    "\n",
    "# Sum series to get the temperature\n",
    "def T_exact(x, t):\n",
    "    out = 0\n",
    "    for n in np.arange(1, N):\n",
    "        out += T_n(t, n) * np.sin(n * np.pi * x / 2)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we plot the numerical and exact solutions at a series of different times. The plot demonstrates an excellent agreement between the numerical solution provided by PyBaMM (dots) and the exact solution (solid lines). Note that in the finite volume method the variable is evaluated at the cell centres."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gwYPgdI959iwEOzu7bE4msoOlpSWtW7dKt9gHCAy8x8SJk7lx40Y2JxNCGKJZs2ZRtmxZli9fTq9evUhMTCQiIoI8efJgZ2fHo0eP2LFjB5A8bi44OBg/Pz8gebyeVqvFxsaGyMjIdM9ftmzZVM9XLx/r5eX1zzqoqb/Wrl0LQPPmzdm9ezehoaGEhoaye/dumjdvnuY6bdu2TZnZc+nSpbRr1y5l37Vr11K6mL4rUvQJIYT4Tzqdju3bd9C+/UcULfoew4aNxMjICHv7vOkeL0spGL6Mfsfm5uZMnjwFN7eyNGrUlBUrVr7xJ/BCiNzj+Zi+51+jRo3i6tWrLFy4kBkzZlC/fn0aNGjAlClTcHd3p0qVKpQpU4auXbtSt25dAMzMzFi1ahWDBg3C3d0dT09P4uLiaNSoEZcuXaJy5cqsWrUq1XXLlClDeHh4SqHXpk0bNmzYQOXKlTl8+PB/5nZwcGDcuHF4eHjg4eHB+PHjU9Y+7dOnD88nmxw1ahR79uzBzc2NvXv3MmrUqJRzHDhwgFatWmXJ45gRmb1TCCFEhgIDA1m8eAmLFy/h3r17ODo60qtXDz77rCelS5eWpRRysVf97t9/vz5//vkXCxcu5vbt2zg4ONC9+6f07dubcuXKqZhaCJGenLBkg5pmzZqFjY0Nffr0yfZrP3r0iK5du7Jv375M3U4WZxdCCPFWtFotGzduomXLNri6lmTy5CmULVuGNWtWEhR0h//973tKly4NJE/YsWDBfIoVc0Gj0VCsmIsUfLnEq373RYoU4dtvR3HjxhV2795BkyaN+fnnXylf3p26dRuwZMlSYmNj1b4LQggBwIABAzA3N1fl2oGBgcyYMeOdX0da+oQQQgAQGhrKokV/MHfuzwQGBlK4cGE++6wnvXv3wtXVVe14Qs89fvyYP//8i99/X8S1a9fIly8fn3/ely++6J/upAdCiOyT21v69JG09AkhhMiUq1evMnDgIIoUcWXEiG947z1X1q9fw927N/nuu0lS8IksUaBAAYYPH8qVKxfYv38P9evX4/vv/4era0m8vLpx6tQptSMKIYTBkqJPCCFyIUVR2L17D61ataVMmQosXLiYTz7pyJkzfvj67qNDh/aYmMhSriLraTQaGjVqyIYNa7lx4wqDBg1ky5Zt1KxZlzp16rN69Rq0Wq3aMYUQwqBI0SeEEAbMx2cFrq4lMDIyw9W1BH/++Rc+PsupVKkKzZu35PTpv5k0aQKBgbf4449FVK5cWe3IIhcpXrw4M2dOJyjoDnPmzOTx4yd06tSVEiVKM3fuPGJjY9P8H/bxWaF2bCGE0Dsypk8IIQxUerMrajQaFEWhfPnyjBgxlM6dO6k2eF2Ilz1fGuSHH6Zz5MhRbG1tiY2NJTExMeUYmR1WiKwnY/r0j4zpE0IIAcCYMWPTLKCtKAqOjvk5d+5vevToLgWfyFGMjY1p06Y1hw/7cujQARISElIVfAAxMTGMGTNWlXxCiHfH2Ng41Tp906ZNy7JzBwQEsH379gz3nzlzht69ewPg6+vLsWPHMnX+pUuX4ubmhpubW8oC7C8bMWIEZcqUoVKlSnTo0IGwsDAA7ty5g6WlZcr97t+/f8ptmjZtSmhoaKayZESKPiGEMEBhYWHcvRuY7r6nT59hZCRP/yJnq1+/HvHx8enuy+j/thBCf1laWhIQEJDy9eLi5W/rv4q+qVOn8tVXXwGZL/pCQkKYNGkSJ0+e5NSpU0yaNCndQs3T05MLFy5w7tw5SpUqxffff5+yr0SJEin3e/78+Snbu3Xrxi+//PLaWV5FRukLIYQBiY6O5qef5vHjjxmv+ePiUjQbEwnx5lxciqZb4Gk0GoYPH8moUSPJnz+/CsmEMFw/j1jNzfNBWXrOEhWLMPDHTzJ9u/DwcGrUqMHmzZspXbo0Xbp0oXHjxvTt25cBAwbg5+dHbGwsHTt2ZNKkSQD4+fkxePBgoqOjMTc3Z8+ePYwfP57Y2FiOHDnC6NGj6dSpU8o1IiMjOXfuHO7u7ty5c4f58+djbGzMX3/9xdy5c6lfv/4rM+7atQtPT08cHByA5OJu586ddOmSugt6s2bNUv5dq1Yt1q5d+5/3v23bttSvX58xY8a89mOWEfmoVwghDEBcXBxz5vxE8eKl+PbbsdStW4cpUyZjZWWV6jgrKyu8vaeolFKIzPH2npLm/7CFhQX16tVj1qw5vPeeG+PHTyQ8PFylhEKIrBIbG5uqe+eqVauws7Nj3rx59OzZk5UrVxIaGkrfvn0B8Pb2xt/fn3PnznHw4EHOnTtHQkICnTp1Ys6cOZw9e5a9e/eSJ08eJk+eTKdOnQgICEhV8AH4+/tToUIFAFxdXenfvz9DhgwhICCA+vXr4+PjkyrX86+OHTsCcP/+fYoW/ffD1CJFinD//v1X3tfFixfzwQcfpPx8+/ZtqlSpwvvvv8/hw4dTttvb2xMfH8+zZ8/e7sFFWvqEEEKvJSYmsmTJUiZP9iYoKIjGjRsxZcpaateuDSS/gI0ZM5bAwHu4uBTF23uKTIAh9Mbz/6vp/R++cuUK48dP4rvvvJk37xdGjhzOoEEDyZMnj8qphdBvb9IilxWed+98maenJ2vWrGHgwIGcPXs2Zfvq1atZsGABWq2W4OBgLl26hEajwcnJCQ8PDwBsbW3/87rBwcE4OjpmuN/LywsvL6/M36EMeHt7Y2JiknJOJycnAgMDyZcvH6dPn6Z9+/ZcvHgxJXuBAgV48OAB+fLle6vrSkufEELooaSkJJYvX0G5cpXo128ARYo4s2/fbvbt251S8EHym+Y7d26SlJTAnTs3peATeiej/8NlypRh9eoV/P33KerUqc3o0WMoXrwUP/00N8OxgEII/ZOUlMTly5exsrJKGSt3+/Ztpk+fzr59+zh37hytWrUiLi7ujc5vaWn5ytv+V0ufs7Mz9+7dSzk+KCgIZ2fndM+1ZMkStm7dio+PDxqNBgBzc/OUgq5atWqUKFGCa9eupdwmLi4OS0vLN7pvL5KiTwgh9MyhQ4epUaM2Xl7dyZPHii1bNnLs2GEaN26kdjQhsl2VKlXYunUTx44donz5cgwePJTy5d1Zt249hrAslRC53axZsyhbtizLly+nV69eJCYmEhERQZ48ebCzs+PRo0fs2LEDgNKlSxMcHIyfnx+QPF5Pq9ViY2NDZGRkuucvW7YsN27cSPn55WO9vLxSTTDz/Ov5mLzmzZuze/duQkNDCQ0NZffu3TRv3jzNdXbu3MkPP/zA5s2bU3Vbf/LkCTqdDoBbt25x/fp1ihcvDiTPuP3w4UNcXV3f4hFMJkWfEELoiRs3bvDhhx/z/vuNefToMX/9tZS///ajdetWKZ8YCpFb1a5dm/3797Bz5zYsLCzo2LETDRo0SnnzJ4TI2V4e0zdq1CiuXr3KwoULmTFjBvXr16dBgwZMmTIFd3d3qlSpQpkyZejatSt169YFwMzMjFWrVjFo0CDc3d3x9PQkLi6ORo0acenSpZSxgi8qU6YM4eHhKYVemzZt2LBhA5UrV041vi4jDg4OjBs3Dg8PDzw8PBg/fnzKpC59+vTh+VriX375JZGRkXh6eqZamuHQoUNUqlQppfVw/vz5Kbc/ffo0tWrVwsTk7UfkyeLsQgiRw4WGhqaMWzI3N2fUqJEMGTI4zQQXQohkWq2WxYv/YNy4iTx+/JhPP+3K1KlTUk22IIT4V25fnH3WrFnY2NjQp08ftaOkMnjwYNq2bUuTJk3S7JPF2YUQwkAkJCQwZ85PlCxZhjlz5tKzZ3euX7/MmDGjpeAT4hVMTEzo168v169f5ttvR7FmzTpKlSrH2LHjM+ziJYTIvQYMGIC5ubnaMdKoUKFCugXfm5CiTwghcqB9+/ZTqVJVvv56GFWrVuHMGT8WLJhPoUKF1I4mhN6wtbXF2/s7rl69yIcftsfb+3tKlSrH8uUrZLyfECKFhYUF3bp1UztGGs+Xp8gKUvQJIUQOcv/+fTp16krTps3RarVs2bKR3bt3UKlSJbWjCaG3ihUrho/PMk6cOEKRIs54eXWncWNPLl68qHY0IYTIFlL0CSGECnx8VuDqWgIjIzNcXUuwdOkypk+fSenS5dm8eQuTJ0/kwoUAmaRFiCxUs2ZNTpw4yvz5P3P27DkqV67OiBHfEBkZmeZv0sdnhdpxhRAiy8hELkIIkc18fFbQr19/YmJiUrZpNBoURaFNm9bMnj0jZbpmIcS78fTpU0aPHsPChYuxt7cnOjqahISElP1WVlYsWDBf1rYUuUJun8hFH8lELkIIkcONGTM2VcEHyWvxODrmZ/PmDVLwCZEN8ufPz++//8bx44eJjo5KVfABxMTEMGbMWJXSCSFE1pKiTwghstndu4Hpbn/69Fk2JxFC1KpVi4SExHT3BQbey+Y0QuRexsbGqdbpmzZtWpadOyAggO3bt2e4/8yZM/Tu3RsAX19fjh07lqnzL126FDc3N9zc3Fi6dGm6x0ycOBFnZ+eU+/c8z/nz5+nZs2emrvcm3n6lPyGEEK/t1q1bWFhYEBcXl2afi4usISaEGooVc0n3w5iCBQuqkEaI3MnS0pKAgIB3cu6AgAD8/f1p2bJluvunTp3K2LHJLfu+vr5YW1tTp06d1zp3SEgIkyZNwt/fH41GQ7Vq1Wjbti329vZpjh0yZAjDhw9Pta1ixYoEBQURGBiIi4tLJu/Z65OiTwghsoFOp2POnJ8YN24iAKampiQm/tu6YGVlhbf3FJXSCZG7eXtPSXec7aNHjxg5chQTJ46XtTFFrnHYezVPrmRtK7djmaLUH/NJpm8XHh5OjRo12Lx5M6VLl6ZLly40btyYvn37MmDAAPz8/IiNjaVjx45MmjQJAD8/PwYPHkx0dDTm5ubs2bOH8ePHExsby5EjRxg9ejSdOnVKuUZkZCTnzp3D3d2dO3fuMH/+fIyNjfnrr7+YO3cu9evXf2XGXbt24enpiYODAwCenp7s3LmTLl1efzxwmzZtWLlyJSNHjsz0Y/S6pHunEEK8Y+fOnaN27XoMGzaSxo0bce3aJf74YxHFirmg0WgoVsxFJowQQkVeXl1YsGB+qr/J+fN/oU+fz/jxxxlUqlSVAwd81Y4phEGLjY1N1b1z1apV2NnZMW/ePHr27MnKlSsJDQ1NWbvO29sbf39/zp07x8GDBzl37hwJCQl06tSJOXPmcPbsWfbu3UuePHmYPHkynTp1IiAgIFXBB+Dv70+FChUAcHV1pX///gwZMoSAgADq16+Pj49PqlzPvzp27AgkL7VUtOi/PXWKFCnC/fv3072P8+bNo1KlSnz22WeEhoambK9evTqHDx/O0sfzZdLSJ4QQ70hCQgJTpkzl++//h729PStW/EWnTp+g0Wjw8uoiRZ4QOUh6f5P9+vWhS5fO9Os3gMaNPenT5zNmzPgRW1tblVIK8e69SYtcVsioe6enpydr1qxh4MCBnD17NmX76tWrWbBgAVqtluDgYC5duoRGo8HJyQkPDw+A1/pbDQ4OxtHRMcP9Xl5eeHl5Zf4OvWTAgAGMGzcOjUbDuHHjGDZsGIsXLwagQIECPHjw4K2v8SrS0ieEEO/A+fPnqVmzDt99503nzp9w+fJ5OnfuJGvuCaFnGjVqyLlzfzNy5HAWL15CpUpV8fU9qHYsIXKNpKQkLl++jJWVVUrr2O3bt5k+fTr79u3j3LlztGrVKt2x8q/D0tLylbf9r5Y+Z2dn7t37tztsUFAQzs7Oac5TsGBBjI2NMTIyom/fvpw6dSplX1xcHJaWlm+U/3VJ0SeEEFlIp9Px448zqF69Fg8eBLNx4zqWLVtKvnz51I4mhHhDlpaW/O9/33PkiC9mZmY0atSUIUOGERsbq3Y0IQzerFmzKFu2LMuXL6dXr14kJiYSERFBnjx5sLOz49GjR+zYsQOA0qVLExwcjJ+fH5A8Xk+r1WJjY0NkZGS65y9btiw3btxI+fnlY728vAgICEjztXbtWgCaN2/O7t27CQ0NJTQ0lN27d9O8efM01wkODk7594YNG1K6lAJcu3Yt1c/vghR9QgiRRW7evEnDhk0YOXIUrVu34sKFANq1a6t2LCFEFqlduzZnzvjx5ZdfMHv2T1StWgN/f3+1YwlhEF4e0zdq1CiuXr3KwoULmTFjBvXr16dBgwZMmTIFd3d3qlSpQpkyZejatSt169YFwMzMjFWrVjFo0CDc3d3x9PQkLi6ORo0acenSpZSxgi8qU6YM4eHhKYVemzZt2LBhA5UrV36tcXYODg6MGzcODw8PPDw8GD9+fMqkLn369El5jhg5ciQVK1akUqVKHDhwgFmzZqWc48CBA7Rq1SpLHseMaBRFeacXyA7Vq1dX5ElXCKEWRVH4/feFDB06AhMTE+bNm4OXV1fpyimEAdu7dx+9evUhODiYsWO/ZcyY0ZiamqodS4g3cvnyZcqWLat2DNXMmjULGxsb+vTpk+3Xjo+P5/333+fIkSOYmLz+dCvp/c40Gs1pRVGqp3e8tPQJIcRbCA4OplWrtnz++RfUrl2L8+fP8OmnXlLwCWHgmjZtwvnzZ+jatTOTJn1H7dr1uHTpktqxhBBvYMCAAZibm6ty7cDAQKZNm5apgu9NSNEnhBBvaNu27SmTOsybN4ddu7anmrZZCGHY8ubNy59/LmHdutXcvRtI9eq1WLDgdwyhF5UQuYmFhQXdunVT5dpubm40bNjwnV9Hij4hhMik+Ph4vv56KK1bt8PZuTB//32KgQO/wMhInlKFyI0+/LAD58+foV69unz++Rd88kmXVGtwCSGE2uQdihBCZMLVq1epVasuc+bMZfDgQZw4cZQyZcqoHUsIobJChQqxc+c2fvhhGhs3bqJy5eocPXpU7VhCCAFI0SeEEK9FURT++GMJVavW4N69ILZs2cjs2TOxsLBQO5oQIocwMjJixIhhHD16EBMTExo0aMx333mj0+nUjiaEyOWk6BNCiP8QHh5O167d+OyzvtSsWYNz5/6mdet3O7WyEEJ/1ahRgzNn/Ojc+RPGj59IkybNCAoKUjuWECIXk6JPCCFe4uOzAlfXEhgZmeHkVAQ3t7KsWbMWb+/v2LNnJ4ULF1Y7ohAih7O1teWvv/5k6dLF+Pufxt29GiNGjEp5bnF1LYGPzwq1YwqRYxgbG6dap2/atGlZdu6AgAC2b9+e4f4zZ87Qu3dvAHx9fTl27Fimzr906VLc3Nxwc3Nj6dKl6R7TqVOnlPvm6upK5cqVAbhz5w6WlpYp+/r3759ym6ZNm2bZ+OB3OzeoEELoGR+fFfTr15+YmBgAHj58hEajYfz4sXz77SiV0wkh9IlGo6F7927UqlWTZs0+YPr0GSn77t4NpF+/5Dd3Xl5d1IooRI5haWlJQEDAOzl3QEAA/v7+tGzZMt39U6dOZezYsUBy0WdtbU2dOnVe69whISFMmjQJf39/NBoN1apVo23bttjb26c67sVF4YcNG4adnV3KzyVKlEj3vnfr1o1ffvmFMWPGvFaWV5HF2YUQ4gWuriW4ezcwzfZixVy4c+emComEEIbAxeU97t1L28VTnltETvDiQt9PF80m4c6NLD2/mWtJ8vf++pXHWFtbExUVlWpbeHg4NWrUYPPmzZQuXZouXbrQuHFj+vbty4ABA/Dz8yM2NpaOHTsyadIkAPz8/Bg8eDDR0dGYm5uzZ88eKlasSGxsLM7OzowePZpOnTqlXCMyMpLq1atz9epV7ty5Q61atTA2NsbR0ZG5c+dSv379V+ZesWIFvr6+/PbbbwB8/vnnNGzYkC5d0v8wR1EUXFxc2L9/P25ubty5c4fWrVtz4cKFNMeGhoZSv379dPdldnF2aekTQogXpFfwAQQG3svmJEIIQxIUdD/d7fLcIkSy2NjYlC6PQEpxNm/ePHr27MngwYMJDQ2lb9++AHh7e+Pg4IBOp6NJkyacO3eOMmXK0KlTJ1atWoWHhwcRERFYWVkxefJk/P39mTdvXprr+vv7U6FCBQBcXV3p378/1tbWDB8+HAAfHx9+/PHHNLcrWbIka9eu5f79+6nW6C1SpAj376f/9w5w+PBhChYsiJubW8q227dvU6VKFWxtbZkyZUpKoWlvb098fDzPnj0jX758mXg005KiTwgh/rF27To0Gk26Cyu7uMii60KIN+fiUjTdD5Xy5rVDURQ0Go0KqYRI679a5N6VjLp3enp6smbNGgYOHMjZs2dTtq9evZoFCxag1WoJDg7m0qVLaDQanJyc8PDwAJLH1v6X4OBgHB0dM9zv5eWFl5dX5u9QBlasWJGqFdDJyYnAwEDy5cvH6dOnad++PRcvXkzJXqBAAR48ePDWRZ9M5CKEyPUSExMZOnQ4H3/cmRIliqdZhsHKygpv7ykqpRNCGAJv7ylYWVml2mZsbExoaBiffNKFiIgIlZIJkbMlJSVx+fJlrKysUiY1uX37NtOnT2ffvn2cO3eOVq1aERcX90bnt7S0fOVtfXx8Uk0w8/yrY8eOADg7O3Pv3r8t9kFBQTg7O6d7Lq1Wy/r161N1LzU3N08p6KpVq0aJEiW4du1ayv64uDgsLS3f6L69SIo+IUSu9ujRIxo39mTWrDl89dWXXLx4joULF1CsmAsajYZixVxYsGC+TLQghHgrXl5dWLBgfqrnliVLFvPjj/9jw4aNeHjU5sqVK2rHFCLHmTVrFmXLlmX58uX06tWLxMREIiIiyJMnD3Z2djx69IgdO3YAULp0aYKDg/Hz8wOSx+tptVpsbGyIjIxM9/xly5blxo1/xzC+fKyXlxcBAQFpvtauXQtA8+bN2b17N6GhoYSGhrJ7926aN2+e7rX27t1LmTJlKFKkSMq2J0+epKzleevWLa5fv07x4sWB5PF/Dx8+xNXV9Q0fvRcoiqL3X9WqVVOEECKz/Pz8lCJFXBVLSxtlxYqVascRQuRSBw8eUhwdnRRbWwdl69ZtascRudClS5fUjqAYGRkp7u7uKV/ffPONcuXKFaVMmTJKRESEoiiKMmTIEGX8+PGKoihKjx49FDc3N6Vx48ZKhw4dlD/++ENRFEU5deqUUrNmTaVSpUpKzZo1lcjISOXZs2dK9erVFXd3d2XlyrSv9xUqVEi5xtWrV5WKFSsq7u7uyqFDh14r+6JFi5QSJUooJUqUUBYvXpyyvXfv3oqfn1/Kzz169FB+/fXXVLddu3atUq5cOcXd3V2pUqWKsnnz5pR9fn5+yocffpjuNdP7nQH+Sgb1kszeKYTIlXx8ltOnz+cULFiQjRvXpho8LoQQ2S0wMJAOHTpy5kwA3t7fMWrUSBnnJ7JNejNB5iazZs3CxsaGPn36qB0llcGDB9O2bVuaNGmSZl9mZ++U7p1CiFxFp9MxYsQ3fPppD2rWrIGf33Ep+IQQqnNxceHwYV86d+7Et9+OpUuXT4mOjlY7lhC5woABAzA3N1c7RhoVKlRIt+B7E1L0CSFyjdDQUFq2bMP06TP58ssv2LNn5ytn7BJCiOxkZWWFj8+f/PDDNFavXkPdug24c+eO2rGEMHgWFhZ069ZN7RhpPF+eIitI0SeEyBUuXryIh0dtDhzw5fff5zN37hxMTU3VjiWEEKloNBpGjBjG9u1buHPnLh4etfH1Pah2LCGEnpOiTwhh8LZu3UatWvWIiorC13cvffr0VjuSEEK8UosWzTl16hj58+enadPmzJ//m9qRhBB6TIo+IYTBUhSFOXN+ol27DylduhT+/ieoU6eO2rGEEOK1lCpVipMnj9KiRXMGDPiSoUOHp0ztLoQQmSFFnxDCIGm1Wr788iu+/noY7dq15eDB/anWxRFCCH1ga2vLpk3rGTx4ELNmzaFDh45ERUWpHUsIoWek6BNCGJyIiAjatGnPL7/MZ8SIYaxdu4o8efKoHUsIId6IsbExs2fPZN68OWzbtp369RsSFBSkdiwhspSxsTGVK1dO+Zo2bVqWnTsgIIDt27dnuP/MmTP07p089MPX15djx45l6vwtWrQgb968tG7dOsNj4uPj6dSpEyVLlqRmzZopkzSdP3+enj17Zup6b0KKPiGEQbl79y516zZg7959LFjwKz/8MA0jI3mqE0Lov4EDv2Dr1k3cvHmLmjXr8vfff6sdSYgsY2lpSUBAQMrXqFGjsuzc/1X0TZ06la+++gp4s6JvxIgRLFu27JXHLFq0CHt7e27cuMGQIUP45ptvAKhYsSJBQUEEBgZm6pqZJYuzCyEMxsmTJ2nX7iPi4uJYu3YVTZtmzdo2QgiRk5w/f57Wrdvz9OlTli9fRrt2bdWOJPTciwt9J50egRJ6PkvPr7GviFG1H195jLW1dZquy+Hh4dSoUYPNmzdTunRpunTpQuPGjenbty8DBgzAz8+P2NhYOnbsyKRJkwDw8/Nj8ODBREdHY25uzp49e6hYsSKxsbE4OzszevRoOnXqlHKNyMhIqlevztWrV7lz5w61atXC2NgYR0dH5s6dS/369V/rPvr6+jJ9+nS2bt2a7v7mzZszceJEateujVarpVChQjx58gSNRsOcOXOIj49n5MiRr3UtkMXZhRC51Nq162jYsClWVlYcP35YCj4hhMGqWLEiJ08epXz5cnTo0JEZM2ZhCB/ii9wtNjY2VffOVatWYWdnx7x58+jZsycrV64kNDQ0Ze06b29v/P39OXfuHAcPHuTcuXMkJCTQqVMn5syZw9mzZ9m7dy958uRh8uTJdOrUiYCAgFQFH4C/vz8VKlQAwNXVlf79+zNkyBACAgKoX78+Pj4+qXI9/+rYsWOm7t/9+/cpWrQoACYmJtjZ2fHs2TMAqlevzuHDh9/2IXwlk3d6diGEyAazZs1m6NAR1K5di02b1suC6+KN7V11ikUTNvEkKATHIg70ntSOpp1q6N01hOErVKgQvr776N69F8OHj+Tu3bvMmjUDY2NjtaMJPfdfLXLvyvPunS/z9PRkzZo1DBw4kLNnz6ZsX716NQsWLECr1RIcHMylS5fQaDQ4OTnh4eEBJE+E9F+Cg4Nf+b7By8sLLy+vzN+hTChQoAAPHjx4p9eQok8IobeSkpIYOXIUM2bM4sMPO+Dj8ycWFhZqxxJ6au+qU8wc6EN8bAIAj++FMHOgD8ArizJFUdBpkzAy1vzn+NE3vYYQ6bGysmL16hUMHz6SWbPmEBz8kGXLlsjzoDAoSUlJXL58GSsrK0JDQylSpAi3b99m+vTp+Pn5YW9vT8+ePYmLi3uj81taWr7ytj4+Pvz4Y9pCuGTJkqxdu/a1r+Ps7My9e/coUqQIWq2W8PBw8uXLB0BcXByWlpaZD58JUvQJIfSKj88KxowZy927gVhZWRETE8PAgQOYM2eWfMItMk2n1fHsYTiP74Xy84g1KcXYc/GxCUwfsIyVM3YTFxOPNkFHYoIWbaIWbWIS2gQt2sR/100zNjHC1MwEEzNjTEyMMTEzwdTMGDNLM2zsrbj2dyAJcYlprvHrN2spV+M9HJ3tMTWTl2bx+oyMjJg5czrOzs4MHz6SCxcuEB0dTVDQfVxciuLtPQUvry5qxxTijc2aNYuyZcsydepUevXqxfHjx4mIiCBPnjzY2dnx6NEjduzYQcOGDSldujTBwcH4+fnh4eFBZGQklpaW2NjYEBkZme75y5Yty4wZM1J+trGxISIiIuXnrGrpa9u2LUuXLqV27dqsXbuWxo0bo9FoALh27VpKF9N3RV5ZhBB6w8dnBf369ScmJgaAmJgYTE1NUwZdC/EyRVF49jCcwCsPCbz2kEd3n/H4fiiP74XyJCiUZ8FhJCW9eixUYrwWp/fyY5nHHFOzfws5E9Pk4s7UzARjEyOSdEkkJujQJmiTvydqU36Oi0kgMiwmTcH3XNiTSLpVGA+AQ0FbHIvYU6CIPQWKOFDQxYEibgUpVsaJAkXtZTZaka5hw4Zw69Ytfvllfsq2u3cD6devP4AUfiLHez6m77kWLVrQq1cvFi5cyKlTp7CxsaFBgwZMmTKFSZMmUaVKFcqUKUPRokWpW7cuAGZmZqxatYpBgwYRGxuLpaUle/fupVGjRkybNo3KlSunmcilTJkyhIeHExkZiY2NDW3atKFjx45s2rTptSdyqV+/PleuXCEqKooiRYqwaNEimjdvzvjx46levTpt27ald+/edOvWjZIlS+Lg4MDKlStTbn/gwAFatWqVdQ9mOmT2TiGE3nB1LcHdu2mnNC5WzIU7d26qkEhkp1eNhVMUhcf3Qrh5Poi7Vx4SeDX56961h0RH/Nttx9TchAJFHSjgbP9vYVXUAUdne37s/ychjyLSXLdAUQdWXPHOkvvQpcwYHt8LSbM9r6MNfSe3/6cgDeFJUGhKcRoXHZ9ynLmlKUXdCuJSuhAuZZxwKV2I4uUL41yyQJpiUMYO5j7yHCneVHozQeYms2bNwsbGhj59+mT7tePj43n//fc5cuQIJiav3x6X2dk7pegTQugNjcY0g+0akpIS0t0nDMPLY+EATMxMqNGsPInxiVw7E0j403+n+s5XyO6fwqgQLqULUax0IYqWLkS+QnYp3Wle5xrmlmYM/dkry4qlzF5DURQinkUTeC25iE0uaIMJvPqQR4H/Fo9WNha4VXGhdBUXSlUtxtPgMBZP3JyqZTGr74vIeYyMzNKdxVOeI8V/ye1FX1xcHGvWrKFbt27Zfu3r169z//59GjZsmKnb5fiiT6PRtADmAMbAQkVRpr203wVYCuT955hRiqJkvJoiUvQJkRucOHGCunXfJykpKc0++RTbsOm0Ojq5fUvo47StcADFKzhTqmoxSld1oUSlohQrXQjrvFZvdC19mr0zNjqee9cecet8EFfP3OXa34HcPBdEYoI2w9tkZaulyHkyaulzdMzP48fBKiQS+iK3F336KEcXfRqNxhi4BngCQYAf0EVRlEsvHLMAOKMoyq8ajaYcsF1RFNdXnVeKPiEM2549e2nf/iOsra2JiIhINcuWlZUVCxbMl/EqBkSbqOPambucPXSds0euceH4TWKj4jM8fl/0r9mYLmdLTNBy51Iw/etOzfCYbqNb4l7PjXI1i2NuaZaN6cS79vK4Z0ie6EVRFBYtWkCvXj3VCydytMuXL1OmTJkMe0KInEVRFK5cuZKpoi+7J3KpAdxQFOUWgEajWQm0Ay69cIwCPF9Uww54t4tWCCFytPXrN9Cly6eULl2K3bt3sG/fAcaMGUtg4D2Zmc6APLj9hFO7LnJy1wXOHb2RMo6tWFknPLvUxHf9aSKeRae5XYGiDtkdNUczNTPBrXJRChR1SHfsoImpMT7/28Gy77djamZC2RrvUcOzPDVbVOC98oXlDZ+ee/5c+OJz5Lhx41i9ejWffdaXsLAwhgz5Wt2QIkeysLDg2bNn5MuXT54HcjhFUXj27Fmml2bJ7pa+jkALRVH6/PNzN6CmoihfvnCME7AbsAfyAE0VRTn9qvNKS58QhmnJkqX07t2PmjVrsG3bZuzt7dWOJLJIYoKW80dvcHLXBU7uusC9a48AKOJWgGqNy+JevxSV6pbEvkDyZ4DZMd7OkLzq8arVoiIXjt/g7OHr/L3/MjfOBQFQoIg9NZpXoGbz8lR5vzSW1rLWm6GIj4/Hy6s769atZ/z4sUycOF7e2ItUEhMTCQoKeuO17kT2srCwoEiRIpiapp7rICd173ydom/oP7lmaDSa2sAioIKiKEkvnasf0A/AxcWl2t27d7PrbgghssGcOT/x9dfD8PRsyoYNa8mTJ4/akcRbigqL4di2cxzdepbT+y8TGxWPqbkJleuXokbz8tRsXgHnEgUyvL3MRpk5r/t4PQ0O49Tui5zadYHT+68QExmHqZkJleq5Uad1JRq0q4JDITsV7oHISlqtls8/H8DixUsYNGggs2fPlOU/hDAwOanoqw1MVBSl+T8/jwZQFOX7F465SHJheO+fn28BtRRFeZzReaWlTwjDoSgKkyZ9x6RJ3/Hhhx1YvnwZ5ubmascSbygqLIajW89ycMPfnN53GW2iDkdne2p9UIEazSsktyjlkd9vTpGYoOXC8Zuc3JncAht49SEajYZK9UrSoENVKQD1nKIoDB8+kpkzZ9O9+6csWvR7pqaIF0LkbDmp6DMheSKXJsB9kidy6aooysUXjtkBrFIUZYlGoykL7AOclVcElaJPCMOQlJTE0KHDmTNnLj17duf333+TNyR64OUWJa9vWmBqapKq0CtQ1IH3O1Tl/Q+rUqa6q3Qt0xN3LgdzaMNpfNf/zd3LwWg0GirWLcn7HapSv10Vzhy6Kq2vekZRFLy9v2fcuAm0b9+OFSv+yvTYICFEzpRjir5/wrQEZpO8HMNiRVG8NRrNZMBfUZTN/8zY+TtgTfKkLiMVRdn9qnNK0SeE/tNqtfTt+zlLlvzJ119/xYwZP0rXIz2Q3tix554Xeg0/qkbpasWk0NNzzwvAg+v/5s7lYNAkr/+mJP37PkLGWeqPefN+ZtCgr2nSpDEbN67D2tpa7UhCiLeUo4q+d0GKPiH0W2JiIt269WTVqtVMnDie8ePHSoGgJz4u/g0hj9Kun2dfwJY1t6bJ79FA3bkczKDGPxATkXbSB8ci9qy8mvGSESLnWLbsL3r16kOtWjXZvn0Ltra2/30jIUSO9aqiTz5GF0KoKiEhgc6dvVi1ajU//DCNCRPGSaGQw0WERLNxvi8D6n2fbsEHEPYkQn6PBsy1rBOxkenP8vckKBSfH3fw5H5oNqcSmdWt26esXOnDyZOnaNbsA8LCwtSOJIR4R6ToE0KoJj4+no4dO7F+/QZmz57BiBHD1I4kXuHK6Tv88PmfdHIbzdxhq0hKUrDOa5XusY5FZP08Q5fR79jU3ITFEzfTtcwYJnT5jdP7L2MIvYoMVceOH7F27Sr+/vsMTZs2JyQk7fqOQgj9J0WfEEIVsbGxtG//EVu2bOXnn39i8OCv1I4k0hEfm8DOZcf5osE0Bjb4Hwc3/E3zT2ux4PgYfjv2LYNmdsLc0izVbcwtzeg9qZ1KiUV26T2pXbq/++G/dmPZhcl0GtKM88duMLLNT/SsMpF1P+8nKixGpbTiVdq1a8vGjeu4cOEijRt78vTpU7UjCSGymIzpE0Jku5iYGNq1+5B9+/azYMGv9OnTW+1I4iUPbj1h8++H2LnsGJGhMbiUKUS7fu/j2aUmeWwtUx0r6+flXv/1u0+IS+Tghr/Z/PshLp28hbmlKU061aBt3/dxq1xUxeQiPbt376Fduw8pUaIE+/btomDBgmpHEkJkgkzkIoTIMaKiomjduh2HDx9h8eLf6dGju9qRxD8UReHC8ZusnrOX49vOYWSsoV7byrTt+z7u9d1kjJ54K9cD7rFl4SH2rTpFXEwCleq58fHgptRqUUFm6s1B9u8/QJs27XFxcWHfvl0ULlxY7UhCiNckRZ8QIkeIiIigZcs2nDhxkj///IOuXbuoHUkAOl0Sx7aeZdXsPVw+dRsbhzy069uANn0bkN8pr9rxhIGJCothx5/HWP/zfh4HheJSphAff9WUpp1qYGZhqnY8ARw+fISWLdtQqFAh9u/fTdGi0iorhD6Qok8IobqIiAiaN2+Jv/9pli9fxscfd1Q7Uq4XF5PAbp/jrJ27j/s3n+D0Xn46DmpC809rY5nHXO14wsBpE3UcXH+a1bP3cONcEPYFbOnwRUPa9mmAjX0etePlesePH6dFi9bky5ePgwf3SeEnhB6Qok8IoaqoqChatGjFiRMnWb16BR9+2EHtSLlaVFgM6389wMb5voQ/jaJMdVc++dqTem0rY2ws3exE9lIUhTO+V1k9ew9+ey9hkceclj3q8MmQZjgWzqt2vFzt1KlTeHp+QIECBfD13Yuzs7PakYQQryBFnxBCNdHR0bRs2YajR4+xYsVf0sKXzV6caCOfU15KVytGwMGrREfEUeuDinT62pOKdUvKeD2RI9w8H8San/ayf7UfRkZGfNCzDkVLFWTNnH0yUZBKTpw4QbNmLXFycsLXdy9OTk5qRxJCZECKPiGEKmJiYmjduh0HDx7Cx+dPOnfupHakXGXvqlPMHOhDfGxCqu2lqxVj6FwvSrpLdy2RMwXfecqK6bvY8edRknSp36eYW5ox9GcvKfyy0dGjR2nevBUuLi4cOLBHZvUUIod6VdEn/XiEEO9EXFwc7dt/hK/vQZYuXSwFnwp+H7shTcEHEPo4Ugo+kaM5ueZn6Dwv7AvYptkXH5vAogmbVEiVe9WtW5dt2zZz584dmjZtLuv4CaGHpOgTQmS5+Ph4OnToyN69+1i8+Hc+/dRL7Ui5SlRYDAsnbOTpg7B09z8JCsneQEK8oZCH4eluf3wvhCcZ/P8W78b77zdg69ZN3Lhxk6ZNm/Ps2TO1IwkhMkGKPiFElvDxWYGrawk0GlPs7fOzc+cuFiz4lZ49e6gdLdeIj01g1azdfFphHCum78Lcyizd4xyLOGRzMiHezKv+r3avOJ4FYzcQGRqdjYlyt8aNG7F58wauXLlKtWo1KVr0PYyMzHB1LYGPzwq14wkhXkGKPiHEW/PxWUG/fv25ezcQgNjYOExNTbG0tFI5We6g0+rYvvQoPdwnsmDsBspUd+W3Y98ydJ4X5papCz9zSzN6T2qnUlIhMqf3pHbp/h8e8L+OvN+hKqtn70n5kCMuJm1XZpH1PD2b8tVXX3L37l2CgoJQFIW7dwPp16+/FH5C5GAykYsQ4q25upZIKfheVKyYC3fu3FQhUe6gKApHNgeweNJmAq8+pKyHK30md6Byg1Ipx7w4e6fMfCj00av+D988H8SiiZs4ufMC+Zzs6P5tKz7oXgdjE2OVUxs2ec4XImeS2TuFEO+URmOawXYNSUny6fu7cPHETX4dtZbLfndwKV2I3hPbUbeNuyy9IHKlc0ev8/u4jVw6eYsibgX43PtDaresJH8P74iRkRnpvX+U53wh1CWzdwoh3hlFUbCxsUl3n4uLzBCZ1R7dC8G75yK+ajKdx0GhDP+lGwtPjaVe28ryBlfkWpXquvHTvuF8t7o/RkZGjPtkPiNbz+HWhftqRzNIGT23Fy1aJJuTCCFelxR9Qoi38t133kRGRmJiYpJqu5WVFd7eU1RKZXhio+P547st9Kw8kSNbztJtdEuWnp3EBz2kK5sQkNzKVKeVO7+fHMugGZ24fi6Iz2t7M3vwcsKeRKodz6B4e0/ByirtmO2iRYuSlJSkQiIhxH+Rok8I8cZmzZrNhAmT6NmzO4sXL6RYMRc0Gg3FirmwYMF8vLy6qB1R7yUlJbFn+Ul6uE/gr2nbqdfGnSUBE+k5tg2WeczVjidEjmNiakz7/g358+wk2n3ekG1/HKV7pfGsnrOXxASt2vEMgpdXFxYsmJ/qOf/jjzty9Ogxvvzyq3S7fgoh1CVj+oQQb2TRosX06fM5H330IStX+qRp6RNv79KpW/w8Yg1X/O9QuloxBv7wMeVrlVA7lhB65e6VYH77dj0nd13AuYQj/ad1pE7LSmrHMjiKovDNN6P58ccZjBo1ku+/91Y7khC5jkzkIoTIUqtWraZLl09p3rwZGzeuw9xcWpyyUvizKBaO38j2JUfJ52RH38kdaNLZAyMj6ZwhxJs6tfsiv45eS+CVh9RuWZGBP36Ck2t+tWMZFEVR+OKLL5k/fwFTp05h9Ohv1I4kRK4iRZ8QIsts27ad9u0/onbtWuzcuS3dcR3izSQlJbHjz2MsHL+R6PBYPvqyCd1Ht8TS2kLtaEIYBG2ijvW/HGCp91aUpCS8vvmAj79qipl5+jMQi8xLSkqiW7ceLF++knnz5jBw4BdqRxIi15CiTwiRJQ4fPkKzZh9Qvnw59u3bjZ2dndqR9NbLa4+17l2P49vPc/nUbSrWLcngWZ15r7yz2jGFMEhP7ofyyzdrObThb4q4FeCrmZ0JfRIpa1pmkcTERDp27MTmzVvw8fmTrl1lfLcQ2UGKPiHEWzt//jwNGjSmQIECHDnii6Ojo9qR9NbeVaeYOdCH+NjU61lZ2VgwaEYnPLvWlOUXhMgGp3ZfZO6wVTy49QQjYyOSdP/OPGluacbQn72k8HtDcXFxtGjRiqNHj7Ft22aaNfNUO5IQBk/W6RNCvJW7d+/SokVrrKys2L17uxR8b2nRhE1pCj4AK1sLmnnVkoJPiGxSo1l5FvmNI4+tZaqCDyA+NoFFEzaplEz/WVhYsGnTesqXL8eHH37MqVOn1I4kRK4mRZ8Q4pWePn1Ks2YtiYmJYdeubRQrVkztSHrv8b2QdLc/exCWvUGEEJhZmBITGZvuvidB6f+titdjZ2fHjh1bKVCgAK1atePq1atqRxIi15KiTwiRoaioKFq1aktgYCCbN2+gQoUKakfSa4qisG3JkQxb8hyLOGRzIiEEZPy3Z2VjKWv7vSUnJyd27dqGRqOhefNWPHjwQO1IQuRKUvQJIdKVkJBAx46d8Pc/zapVy6lfv57akfTa/ZuPGd5yNjMH+lC0VEFMX5ot0NzSjN6T2qmUTojcrfekdphbmqXaZmRsRHRELAPqTePK6TvqBDMQbm5u7NixhWfPntG8eUvCwsLUjiREriNFnxAijaSkJD77rA+7du1mwYJfadu2jdqR9JZOq2P17D30qTGF6wGBDJnblUX+4xj+66cUKOqARgMFijrIhBFCqKhppxoM/dkr1d/kN7/34LvV/YkIiWJQwx+YP3odcTFpx+KK11OtWjU2bFjL1avXaNOmPbGx6XepFUK8GzJ7pxAiFUVRGDZsBLNmzcHb+zu+/XaU2pH01p3LwfzQbylX/75LndbufDWrM46F86odSwiRCVHhsfw+bgNbFx3G6b38jJzfnUr13NSOpbdWr15D585etGnTmnXrVmNiYqJ2JCEMhszeKYR4bdOnz2TWrDkMGjSQ0aO/UTuOXtLpklg9Zy/9607lYeAzxv3Zh8krP5eCTwg9ZG1nyZCfujJz5xAAhraYxa+j1qY7A6/4b5988jE//TSLzZu3MGDAQAyh8UEIfSAfrwghUqxatZqRI0fxyScfM3v2TFk64A08uPWEH/r/yfmjN6jT2p0hP3XFoaCt2rGEEG/JvX4pfj8xhgVjN7B27j5O7bnINwt6UKaaq9rR9M6XXw7k4cNHeHt/j6urK2PGjFY7khAGT7p3CiEAOHz4CE2bNqdGDQ/27NmJhYWF2pH0iqIobF10mPnfrsfYxIgvf/xEFlkXwkD5773EjwOWEfIogq4jWvDpNx9gaiafo2eGoij06NGLZct8WLZsCZ9+6qV2JCH03qu6d0rRJ4TgypUr1KnTgAIFCnDs2CEcHGTpgMx4cj+U6QOW4b/vMtUal02epEWWXxDCoEWFxTBvxGr2LD9JSfeifLOgB8UrOKsdS68kJCTQokUrjhw5ys6d22jcuJHakYTQa1L0CSEy9OjRI2rVqkdMTAzHjx+mePHiakfSK/tWnWLOkJXoEnV8PvVD2vRpIK17QuQiR7YEMGvQcqLDY+k1vg0fD26KkZFMmfC6wsLCqFevIUFBQRw54ivrwQrxFqToE0KkKzo6moYNm3Dx4iUOHtyHh4eH2pH0RnRELD8NWcnelacoX6s4o37vSeHijmrHEkKoIOxJJLMHL+fwpgCqNizNyN97ysRNmRAYGEitWvUwMTHhxIkjFC5cWO1IQuglmb1TCJGGVqulc2cv/v77DCtX+kjBlwkXT97i89re7F/jT4+xrZm1a6gUfELkYnkdbZjg049hv3zKpVO36VtzCke2BKgdS2+4uLiwbdsmQkNDadWqLZGRkWpHEsLgSNEnRC6kKAqDBw9h69Zt/PTTLFl8/TXpdEksm7adrz1noCgwe88wuo9uhbGJsdrRhBAq02g0tOxRl/lHR1PIxYEJnX9j5iAfYqPj1Y6mF6pUqcKaNSs5f/4Cn3zShcTERLUjCWFQpOgTIheaMWMWv/wynxEjhjFw4Bdqx9ELjwKfMbTFTJZ8t4VGHavz2/ExlK8p4x+FEKkVLVWIuQdG0mlIM7b/cZQv6k/jesA9tWPphRYtmjN//s/s3LmLL774UtbwEyILyZg+IXKZtWvX8fHHnfnkk49ZseIvmXAgHXtXnWLRhE08CQrBsYgDtVtWZO/KUyhJCoNnd6Fp5xpqRxTvyNUtJzkxcxORwSHYODlQa2g7SrepqXfXEDnDGd8rfN9nCeFPo+g9qT32BWxYPGlzynNL70ntaNpJnk9eNm7cBKZMmcr333szatRIteMIoTdkIhchcjkfnxWMGTOWu3cD0Wg0lCxZknPn/pa1+NKxd9UpZg70IT42IdV25xKO/G/zVzi55lcpmXjXrm45yYGxPmjj/v3dm1iY0WiKV5YVZdlxDZGzhD+LYuaXPhzZHICRkYakpH/fd5lbmjH0Zy8p/F6iKApeXt1ZsWIljo75efr0GS4uRfH2noKXVxe14wmRY72q6JOVRIUwcD4+K+jXrz8xMTFA8otpUFAQ69ZtkBfPdCyasClNwQeQEK+Vgs/AnZi5KVUxBqCNS+DI1DVoNEZoYxNI0urQaXUkaXUkJf7zXasjSafDyMgIIxNjjEyNk78//zI1xsTcFDMbS458vzbda5yYuUmKPgNll8+aicv70b7ocKJCY1Lti49NYNGETVL0vUSj0eDp2ZRVq1bz5MlTAO7eDaRfv/4A8tolxBuQlj4hDJyrawnu3g1Ms71YMRfu3LmpQqKcran1ANJ7WtRoYG/Ur9kfSGQJXUIiEUHPiH4URvSTcKKfhBPzJCL5++Pkn8NuP3rj82uMjVB0SW+V0alaSfI42mLlaEeeAnbJ3x3tyFMwL7bO+TC1Mn+r8wt1yXNL5shrlxCZJy19QuRi6b1oAgQGysQCL4uLScDcypy4dGbbcyzioEIi8aL/GguXEBVL6O3HRNx7Qnhg8lfEvaeE33tC1MMwXn7HbWJpRh7H5OIqf2lnoh+HkZjO794qvy0d/hyKiaVZqta7f1vzjNBoNCiKQpI26d/WvxdaArXxiSRExrK1/8/EPks7Hb2JpRlGxkY8vXqfmCOXSIiKS5vD0Ra7oo7YFs2PnYtjyr/zvlcQS3vrTD9eIns5FnHg8b2QNNvzOeXN/jB6IKPXKHntEuLNSNEnhIGztbUhIiLtm0wXl6IqpMm5Aq8+ZPKnvxMXE4+xiRE67b+tNuaWZvSe1E7FdOLlsXCRD0LYN/pPrm/zR1Eg5Pp9Iu+nfkNt5WiLbZH8ONcohZ2LI7ZF8mPtZJ/celbADtM8Fmg0mgyvAcnj7eqO+gj7EoX+M6NGo8HY1Bhj04yX8Kg3umP6Y/q+Sz2mLzEmnpinEUQ/DiPqUVhy8fpPEXv/1DWubj6Vqoi1crQlXylnHNwKk8+tMFGPQjn92y508Ykpj9eBsT4AUvippPekdumOF46PTeD8sRtUrFNSpWQ5k4tL0XQ/tCxatIgKaYTQf1L0CWHAFi1aTEREJCYmJmi12pTtVlZWeHtPUTFZzrJ35SlmfbUcc0tTpm0YRHhIVKrZO2WGPfXoEhJ5eiWIQ9+tSjMWLilRx50D58lXyplCVUpQvlMDHEoUwraoI3ZF82e6O+TzYuhdto697jVMrcyTW/NcHNM9jzY+kcj7zwi/94TQW494du0+IdcecHHlIbRx6a9vpo1L4Pj0jVL0qeT5c8iLzy2tPqvH7r+OM7TFLD4b35ZOQz1lRuV/eHtPSTUe/blKlSqplEgI/SZj+oQwUL6+B/H0bEGTJo3p2rUL48dPIDDwnsyA9oLEBC3zR69j43xfKtYtyZglvXEsnFftWLmWoiiE3X7Ew7O3eXzuDo/O3+HplSCSEnWvvN2XV+dnU8KcL0mXRETQU/5qNj7DY/KVcqZAxWIUrORKocrFyVeqMBopNFQTHRHLzC998F13mjqt3flmQQ+s7SzVjpUjPJ95OjDwHkWLFsHNzY19+/azePHv9OrVU+14QuQ4smSDELnMtWvXqFWrHk5OThw7dgg7Ozu1I+U4T4PDmPzp71w8cYuPv2pK3+/aY2yScbc8kfUURSHkRjAPTl3jvt91HvhfJ+ZJBACmeSwoUCG5MClQsRiHvVcR/Sg8zTlsCjvQ48DU7I6e4y1t9C2RD9KOHzOztsCpagkenbtDXFg0AOa2VjhVK4lzDTcKe5TCsWwRjORvIVspisL6Xw7w27frKOSan0nL+/FeeWe1Y+U4iYmJtGrVlgMHfNmzZycNG76vdiQhchQp+oTIRcLCwqhZsy4hISGcOnWM9957T+1IOc7Zw9f4rvsiYqPjGfFrNxp+VE3tSLmCoiiE3nrIvaOXk4s8v+vEhUYBYF3InsIebjjXcMOpagnsixdK1fok69tlzn89XoqiEBH0lId/3+K+X3LRHX7nMZBccBeuVoLCHqUoUrsMBcoXlZbAbHLu6HUmf7qQ2Kg4hv3yKY0/9lA7Uo4TFhZG7dr1efz4MadOHaNEiRJqRxIix5CiT4hcQqfT0bp1O/bt28/+/bupV6+e2pFyFEVRWDdvP7+NWU/h4vmZtKI/rmWd1I6l9141S2RCVCxBJ65y99BFAg9fTGl9snF2oLBHKZxrlMK5hhu2RfKnmlQls9cRaWX28Yp6FMYD/+s8OHWd+37XCL35EABLBxtc6pXDpX45XOqVw9LB5o2vIf7b0+Awvuu2kAvHb/LRwMb08/4Qk1dMDpQb3bx5Ew+P2hQuXJjjxw9jY2Pz3zcSIheQok+IXGLkyFH8+OMMFiz4lb59+6gdJ0eJjYpj+hd/4bvuNPXaVmbkb93JYyvjZt5Wei1KxmYmvNfUnZinETz8+yZJ2iRMrcwpWqcMLvXL41K3HLZFZaH7nC7maQT3jl7m7uGLBB65lNwqq9FQoLwLLvXLozGGv3/fkzJDKEjra1ZJTNDy27fr2fDrASrWLcn4P/vgUEi66b9o7959tGjRijZtWrNu3WqZAEcIpOgTIlf46y8funXrycCBA5g37ye14+QoQTceM77zfO5dfchnE9vReWiz/2xVEq8no7FjAPnLFsWlfjmK1S9PocrFMTaTCaP1lZKUxOOL9wg8dIG7hy/y6OxtlKT03z/IOMuss3flKWZ++RfWdlZM8OlL+VrSlfFFc+b8xNdfD2P8+LFMmjRB7ThCqE6KPiEMnJ+fH/XrN6J27Vrs3r0DU1NTtSPlGH57LjGl50KMjI0Yt7QPVRuVUTuSQdAlJBJ0/Cpb+s3L8BiZVdNwxYVHs7DGsAz39/t7NmZ5LLIxkeG6eT6IiV0X8PheCIPndKFlj7pqR8oxFEXhs8/6sGTJn6xdu4qPPvpQ7UhCqEqKPiEMWHBwMNWr18LMzAw/v+Pkzy/d5iD5zcCan/bx+9j1uJYrzHerB1CoWD61Y+k1XaKOe8cucW2LH7f3nyMxOg40QDovI9LaY/he1cprbGaCS71ylGpTA9dGlTC1NMvmdIYlIiSaKT0WcXr/ZToMaEj/7zvKOL9/xMfH07BhE86dO8+xY4dwd3dXO5IQqpGiTwgDFRcXR6NGTTl//gLHjh2SRWv/ER+bwMwvfdi78hQNOlRl5G/dscyTuYW6RTIlKYngv29xbespbuw4TVxYNOa2VhT3rEyJZlWICYng0KRVMqtmLpTueE5zUyp/1hRtdDw3dp4m+nE4plbmFG9amVKtPShSpyzGUqy8EZ1Wx29jNrBu3j6qvF+accv6YJfPWu1YOUJwcDAeHrUxMTHBz+84jo6OakcSQhVS9AlhgF7s1rJu3Wo+/LCD2pFyhCcPwpjQeT5XT9+l57g2fPrNBzJ+7w08vRLEta1+XN/mR+SDEEwsTHmvsTulWnvgUr8cxmb/diGWGRxzr1f97pN0STzwu861rae4uesM8RExWNhbU7JFVUq1roFTtRLyt/kGdv11nFmDlpO/sB2TVw2geAVZzw/+HeZQq1ZN9uzZKcMcRK4kRZ8QBuj5APYJE8YxceJ4tePkCBdP3mJil9+IjY5n9KJe1G0t3XwyIy48mmtbTnF53TGeXLqHxtgouYteKw/ea+KOmbWM0RJvRpeQyN1DF7m+zZ/b+8+ijUvEzsWRsh/VoUyHWlgXtFc7ol657HebCZ1/IzoyjtELe1KvbWW1I+UIzyc0++KL/vz881y14wiR7aToE8LA7Nu3n+bNW9K2bRvWrl0lU1UDO5cdZ/ZXy3EsYs93q/rjWq6w2pH0gpKURNCJq1xae5RbewLQJWhxLFeUsh/Vwa1l9VRrsgmRFRKi4ri1N4DL645x/9Q1NEYaXOqXp+xHdXivUSWZ5fU1PQ0OY0Ln37jif4ceY1rRbXQraTkFRoz4hunTZ/Lbb7/Qr19fteMIka2k6BPCgNy9e5dq1WpSqFAhWZQWSEpK4vdxG1k9ew9VG5Zm3LK+2DrkUTtWjhf5IITL649xef0xIu+HYG5nRak2NSj3UR0cy7moHU/kEmF3H3Nl/XEubzhO9KMwLOytKd2uJuU61iWfm3xw818S4hKZOciHPctP0rBjNUbO7455Lp80R6fT0apVW/bvP8ChQ/upVauW2pGEyDZS9AlhIGJjY6lX731u3LiJv/8J3Nzc1I6kqtjoeL7v/QdHt5ylbd8GfDn9E4xNZJKIF7045sq6kD2lWnsQevMhd3zPoyhQtE4Zyn5Uh+JNK2NiLmNghDqSdEncO3KJS+uOcnv/OZISdRT2cKNi1/fRJiRyas4WGTOaAUVRWDVrDwvHb6RM9WJMXtk/1y/kHhISQvXqtUhISOD06ZMULFhQ7UhCZAsp+oQwAC9O3LJ58wbatGmtdiRV7F11ikUTNvH4XggmpsbotEl88UNHOgxoJF2bXpLe7IoApnksqNStIeU/rodtEVniQ+QssSGRXF5/nAsrDhER9DTNfpkdNn1HNgcw9bPFmFuZYWJiTOjjCByLONB7UjuadqqhdrxsFxAQQO3a9alZs4ZM7CJyjVcVfTIQSAg98dtvC1iy5E/Gjx+bqwu+mQN9eHwveW0wbaIOY1NjbPNZS8GXjqP/W5em4AMwt7Wk9pD2UvCJHMnSwYaqfZrx6e7JWDqkXZJAG5fA8Rkbsz9YDlevbWW6jGhBxLNoQh5FoCjw+F4IMwf6sHfVKbXjZbvKlSvz++/zOXjwEN98M1rtOEKoToo+IfTA8ePH+eqrIbRs+QETJoxTO45qFk3YRHxs6iJGm6Bl0YRNKiXKeZSkJG7vP8f6bjOIeRKR7jFRD0OzOZUQmWdkbERsaFS6+6KCQ7my8QS6BG02p8rZtv9xNM22+NiEXPsc+emnXgwaNJBZs+awYsVKteMIoSop+oTI4R4+fEjHjp0pWrQof/21NNfO1KkoSkoL38ueBKW/PTfRxiVwYeUhfFpOYtuAX4gMeoa5rWW6x9o4OWRzOiHeTEb/V41MjNn7zRL+bDKG0wt2ERcenc3JcqaMngszeu7MDaZP/4F69erSp8/nnD9/Xu04Qqgmd757FEJPJCYm0qlTV0JDQ9mwYQ329rlzLSttoo6ZX/pkuN+xSO4tYmKeRXBy7haWNPwW3wnLMbUyp9mM3ny6+zsajO+MiUXqmfxMLMyoNbSdSmmFyJxaQ9ul+3+48ffdaPP7IBxKOnF8xgaWNvyWQ1NWEXEv7RjA3CSj50IzC1NiIuOyOU3OYGZmxurVK7Czs6NDh48JCwtTO5IQqpDFcITIwUaOHMWhQ4fx8fmTSpUqqR1HFTGRcUzu9jt+ey5Rp3Ul/PdeJiEuMWW/uaUZvSflviImPPAJfy/czZUNx9ElaHFtVIkqnzWlsIdbyvjG5xNdPJ+9U2Y+FPrmv/4PF2tQnqdXgjizeA8XVhzkvI8vJZpVoWq/FhQon/uWHuk9qR0zB/qk6gZvYmpMYoKWIc1m4L1+IPmd8qoXUCVOTk6sXbuShg2b8umnPdi8eUOu7TUjci+ZvVOIHGr58hV4eXXn66+/YtasGWrHUcXT4DDGfPgzty4+4Os5XWjVq17K7J1PgkJy5cx0z64/4PRvO7m+zQ8jE2PKdKhN5R5NsC9RSO1oQqgq6lEo55b5cmHlIRIiYynWoDzV+3+AU7WSakfLVuk9R9ra52Fyt9+xyWvF9xu+xLVc7lwD8ZdffmXgwK+YOHF8rh4fLwyXLNkghJ45f/48NWvWxcOjOnv37sqVU03fufSA0R/+TERINOOX9aFm8wpqR1LV4wt38Z+/g1t7AjC1MqdC5wZU7tWUPAVy93pcQrwsPjKW88sPEvDHXuJCoyhcw43q/T+gaJ2yuXqW3+sB9/j2w3nExyUyacXnVHm/tNqRsp2iKPTq1ZulS5exbdtmWrb8QO1IQmQpKfqE0CORkZFUr16LiIgIzpzxo1Ch3NeCc+bgVSZ0+Q1zC1O81w2kVJXc103ruQf+N/Cfv4PAwxcxt7Wi0qcNqdS9MZb2aaeyF0L8KzEmnourj3Bm0W6iH4dTsJIr1ft/gGujimhyade+R4HPGP3hz9y/8ZgR87vTtHPu6SXxXGxsLLVr1+PevSDOnPHDxSX3vr4IwyNFnxB6QlEUOnf2Yt269ezfv4cGDeqrHSnb7Vt1ih8+/5PCJRyZtuFLCrrkUzuSKh743+DkT1u4f/Iqlg42VO7VhIpd38fMOv0ZOYUQ6dMlJHJ5wwn+XrCLiKCn5CvtTM2v2vBeE/dc2fIXGRrNhK4LOHvoGp9NbEvX4S1y3eNw/fp1qlevRZkypTl82BczM7P/uokQeiFHFX0ajaYFMAcwBhYqijItnWM+ASYCCnBWUZSurzqnFH3CUMyb9zODBn3NtGlT+eabEWrHyVaKorBi+i4WTdyEe303Jq34HBv7PGrHynaPzt3m5JwtBB65hFV+W6r2bU75TvUxtZQ3JUK8jSStjmtb/fD7ZRvhd59QoEIxan7VBpcG5XNd0ZMQn8j0/svYt9qPVp/VY/CszhibGKsdK1utX7+Bjz76hEGDBvLTT7PVjiNElsgxRZ9GozEGrgGeQBDgB3RRFOXSC8e4AauBxoqihGo0mgKKojx+1Xml6BP6zMdnBWPGjOXu3UAAqlSpgr//iVw1s5hOl8TPI1az6beDNOpYnZELumNmbtjjGK9uOZlqRsLynerxMOAOdw6cwyJvHqr2bU5Fr4ZS7AmRxZK0Oq5sOonfz9uIvP+MQlWKU2twW6KfhueqmW6TkpJYPGkzK6bvonbLioxd2gcLq9z1fDN06HBmzZrDl18OZMuWLQQG3sPFpSje3lPw8uqidjwhMi0nFX21gYmKojT/5+fRAIqifP/CMT8A1xRFWfi655WiT+grH58V9OvXn5iYmJRtlpaW/P77b7nmBSchPpH/9V2K77rTfPxVU/p5dzD4gvfqlpMcGOuDNi4h1XYTSzOqf96CSt0aY2ZtoVI6IXIHXYKWy+uP4f/rDqIehqIx0qAk/fueyMTCjEZTvAy68APYtOAgc4euonyt4kxZMyBX9bBITEykfPlKXL9+I9V2KysrFiyYn2teh4XheFXRl93vrJyBey/8HPTPtheVAkppNJqjGo3mxD/dQYUwSGPGjE1V8EHyIPMxY8aqlCh7RUfE8m2Hn/Fdd5rPvT+k//cfGXzBB8lrjr1c8AFY2FlRfUBLKfiEyAbGZiZU6NyAT3dPxtzWKlXBB6CNS+DEzE0qpcs+7fq9z7hlfbh6+i5fe87gyf1QtSNlG1NT0zSvwQAxMTG55nVY5B45cXF2E8ANaAgUAQ5pNJqKiqKEvXiQRqPpB/QDZOYlobcCA+9larshCXkUwegO87h14T7fLOhBM69aakfKFnHh0UQ+CEl3X9SjsOwNI9KIPLSbUJ/5aJ8+xiR/Aey9+mPToJneXUO8PhNzU+Ij077xBzL8WzU073eoiq29FeM7/8ZXjX9k2qZBFCvjpHasbPHgQXC623PD67DIXbL7I/X7QNEXfi7yz7YXBQGbFUVJVBTlNsljAN1ePpGiKAsURamuKEp1R0fHdxZYiHepQIH0/++6uBRNd7uheHDrCYObTifo+iOmrBmQKwo+bXwify/czbKmGS8IbOPkkI2JxMsiD+3m6a/T0D55BIqC9skjnv46jchDuzO8jZKUhJKYQFJcLEkJ8Sg6Ha8aNvEm1xDv3qv+9o79uJ648OhsTKOOKg3LMHPnUBITdAz2nMGlU7fUjpQtMnq9NfTXYZH7ZPeYPhOSi7gmJBd7fkBXRVEuvnBMC5Ind+mh0WjyA2eAyoqiPMvovDKmT+ijBw8eULZsRSIjI1O9STT0sQTXA+4xqv1cdLokpq77gnI1iqsd6Z1K0iVxdfNJTs7ZTFRwKMUalMepekn8f9mRqotnbhk/lBMoWi26iDB04aGpvkJXLUKJTafFx9gYYzsHlMQEFJ0WdLqU72T0GmpigsbIOOW7xtQUI0srEh8Hg1ab5nCjPNbk+2wwxnb2/3w5YGyXF41p7ppYQy3pjbM1NjelQAUXgv++hbmtJdX7f0BFr4aYGPgkUw9uPeGbdnN5FhzGhL/6UbNFBbUjvVPpja23sLBg4cIFBvs6LAxXjpnI5Z8wLYHZJC/ZsFhRFG+NRjMZ8FcUZbMmed7kGUALQAd4K4qy8lXnlKJP6ButVkvTps3x8/NnwoRx/PLLr7li1rAzvlcY3/k3rO0s+d/mr3ApbdgLzweduMqR79fw9EoQBSoWo87wDylSqzSQdvZOQ58pMCu8TrdIRadDF/oU7ZOHaJ88IvHJw5R/a58+RBcaQlJURKavbd24FUbm5mBkjMbEBI2xccq/MTJGY2yCkqRLVRAqOi2KTgc6LUpCAkmxMUQf25+p6xpZWWOc1x6T/AUxcSyEiePz74WSt+UrgMY0/SJEupFmTkZ/k0+vBHFs+gYCD1/EprADtYd3wK1ldYNe5uHF7vcjfu1m8L0xXpxF29jYmPz583Pt2iVsbW3VjiZEpuSoou9dkKJP6JsJEyYxefIUli5dTPfu3dSOky2ObA5gSo9FOJdwZNqmQTg626sd6Z0Ju/uYYz+s49bes9g456PO8A6U/KCaQb9JfNeed4tU4uP/3WhqinWD5hjnsSHx/l0SHgSifRyc3AL3AiMbu38LJvv8Ka1pRnZ5X2hZsydoaA90T9OuEGTiWBCX39Znyf0I/PzD5K6dLzHO50jh7+ahC0/bAqkLfZZSuOrCXur0otFgkq8Aps4umDoXS/5e2IWEoDuELPsFEv5tudKYm5N/wCgp/N5Q0PErHPnfOp5evodT1RLU+/YTClYspnasdyY6IpaJXX7jb9+rfPHDx3w0sLHakbLFkSNHaNiwKZ988jE+Pn/K87bQK1L0CZGD7N9/gKZNm9O9+6csWbJY7TjZYrfPCX4csIzSVV2Yuv5LbB0Mc0rw+MhY/H/dztk/92NsakK1/h9QuWcTg+8O9i4lxUaTcOcmD78fSVJUZLrHaMzMMHUqimlhF0ydivzbElYguTXMyMLyta6VXmGZ1YXS214jKSEe3dPHyS2YTx8lF4PBQSQ8CCTxfiBKXOwrb2+cz5Fiv29827uRayXpkriy/jjHZ20k9lkkZT6sTa2v22FdMK/a0d6JhPhEpvZazOFNAXT/thXdv22VK4ogb+/vGTt2PAsX/kbv3p+pHUeI1yZFnxA5xOPHj3F3r0bevHnx8zuOtbW12pHeuQ2/HmDe8NVUbViayav6Y2mAyxEk6ZK4vO4oJ2ZvJjYkirIdalNrSDvyFLBTO5pe0UWEEX/tEvG3r5Fw+zrxd66jffjyXF9pvbf2MJosWupDn2fvVBQFXchTEh8EEjzhqwyPM85XAPP33DBzLYnZe25YlKqAST6ZEC0zEqJi8Z+/k4Al+zA2NaZav+ZU7tUUEwvDG4Op0+qYMdCHXX8dp8OARnzxQ0eDX1pHp9PRokUrjh49hp/fccqXL692JCFeixR9QuQASUlJtGzZBl/fg5w6dYxKlSqpHemdUhSFZdO2s3TKVuq2cWfskt6YWRhei9d9v+scnrKKp1eCcKpWkvrffkyBCobb5SurKImJJNy9Qdy1i8Rfu0jc9Utog4NS9psUcsbc1Q2zf4qTp7/9iC7kaZrzZGXXS0OSUTdSozzWWFWtTfyd6yTevwdJyV1hjfMVwMKtHOalymNRujxmxUtjZG54H9BktfDAJxz9cT23dp/BxtmBuiM/okTzqgbXGpaUlMSv36xl/S8HaOZVi+G/fIqxibHasd6phw8fUrlydfLly4ef33GsrKzUjiTEf3pV0ZcT1+kTwiD9+OMMdu3azfz5Pxtkwbd31SkWTdjEk6AQ8jvb41rWCb89l/DsWpMRv3YzuDcI0Y/DOfrDOq5tOYVNYQeaz+qTa8ftvU7LVVJsNHGXzxN78Qxxl86ScPsqyj/jzYzz5sO8VHlsm7TBvHR5zN8rhZFVnpduH5Nut0h7r/7v/g7qIXuv/uk+Xvn6Dkv53STFx5Nw5zrx1y8Rd/0S8dcuEn3CN/lgI2PMXEtiUbYSluUrY1GuMsa2edNcJ7dPFmPn4kjLuZ8TdOIqh6euZufg3ylatywNxnbCvrjhTFRlZGTEFz98jI29FUu9txEdEUud1pVYOmUbT4JCcCziQO9J7WjaqYbaUbNMoUKF+OuvpTRr9gFfffU1CxcuUDuSEG9FWvqEyAbHjh2jQYPGfPhhB1atWm5whcHeVaeYOdCH+NiEVNurNy3L9xu+NKiuQElaHef+8uXkT1vQJWip2qcZ1T5vgaml4XXreh0ZjVFz+GwIpvnyE3vhDHEXzxB/82pyq5KJCeYlymBRqgLmpctj4VYO4/wFX+tvIrcXGJn1Jo+XLiyUuOv/tL5eu0j81QsoCcm/W7NiJbAoXwWL8pWxLFeZmLN+73wMpD5J0uq4sPIQJ2ZvRhuXQOVeTfEY0BJTK3O1o2WpdT/v55eRa9AYaVCS/n0PaW5pxtCfvQyq8AMYO3Y83t7f4+PzJ127GubM2sJwSPdOIVQUEhJClSoeGBsbc+aMH3Z2hjfOq0uZMTy+F5Jmu2MRe1ZenapConfjvt91Dk5eQci1B7jUL0+DsZ+Q17Wg2rFUlVE3whQmJli4lfunWKiCRekKrz2xilCfkphI/M0rxF74m7iLZ4i7ch4lPi55p7EJ6NKuOZjbu9zGPI3g2IwNXFl/HGsne+qN/pgSzaoY1Id97YsOJzIk7YL1BYo6sOKKtwqJ3h2tVkujRk0JCDjL33+fws3NTe1IQmRIuncKoRJFUejdux/BwcEcPXrQIAs+gCdBaQs+gKf3Q7M5SdZ4eb2uKn08eRRwh6ubT2JT2IGWP/fnvSbuBvUm7k08X0YgI06TfsK8VHkZG6bHNKamWJSpiEWZitCxB4pWS/zNK8RdPEPIX/PTvY32ySMURcm1fx9W+W1p+n0Pynesy8HJK9n51QKK1itHg7GdeHzhjkGszxkVmrbgg4xfC/SZiYkJy5cvo3Ll6nTq1JXjx49gbm5Yrbcid5CiT4h3aN68n9m4cRMzZ/6Ih4eH2nHemXyF7dMt8ByLOKiQ5u1c3XKSA2N90MYld1WNfBDCocmr0BgbUX1Ay1zdlVPR6Yi/doHok4eJ+fs4iUF3MjzWxLEglhWrZV84kS00JiZYlK6ARekKROzakGHRf+/zj7CsVps8HvWxrFgVjWnu+5txqlaST9aN5vyKQ5ycvYnlrSYBoOiSgOTnlgNjfQD0rvBzLOKQQe8O/XvOfx1FixZlyZJFtG3bgREjvuGnn2arHUmITDOcgTZC5DBnzpxh+PBvaN26FV9/PVjtOO9MVHgs5hZpPz8ytzSj96R2KiR6Oydmbkop+F5klc+GWl+3zXUFX1J8HNGnDvN43lTu9m7LgzFfEL59DSYO+XHoOQj7bl+AWerHRCZYyR3svfqjebnFw8wM68atMCteiqiDu3g4ZRh3erbi0fRxRB3ajS46/bUWDZWRiTHu3Rrx6a7JGJuZpBR8z2njEjgxc5NK6d5c70ntMH/5uVADHQc1USdQNmjTpjVDhgxm7tzkD3OF0DfS0ifEOxAdHU2XLt3Inz8/f/yx0GC7OYU/i2JUu7k8CgyhwxeNOLrlrN7P5Bb5IP3uSdFPwrM5iXp0kRHE+B0m+tRhYgNOoSTEY2Rl/U/LTT2sqtZONbumST5HmWAlF3r+O87od5+UEE/cudNE+x0mxu8I0cf2g7ExluWrYFWjPnlqvp9r1ge0ym+b7odJAJHB+tcl8vlz+/MZm/M62hITGcfaufuo1aICziUKqJzw3Zg2bSqHDh2md+9+eHhUx9nZWe1IQrw2mchFiHegX7/+LFy4mL17d9G4cSO147wTIY8iGNnmJ4JuPGLi8n7UalFR7UhvLfDIJbb0m5fm03gAm8IO9DhgOJPSvCwpOoroU4eJOrqP2HN+oNVinL9gcpFXoz6W5SqjMTW8dRZF9lCSkoi/fonoU4eJOXWYxPt3QaPBomwl8tRtQp7ajTDJa5hdA59b2ujbdD9Ussibh94npuv9h4PXzgTyTbufMDU14cdtgylWxkntSO/EtWvXqFq1BjVqeLBnz06MjQ1rOSKh32T2TiGy0bp16+nYsROjRo3k++8Naxaz5548CGNEq9k8CQrlu9UDqNqojNqR3krMswiOfL+Wa1tOYeVoS1xYDEmJ/85KaGJhRqMpXno37ua/JMVGE+N3lKij+4g5cxK0iZg4FiJP3cZY12mMWYkyev9GVORMCUF3iT62n6ij+0i8dxuMjLAoVxnruk3IU+t9jO3s1Y6Y5V4eLwyARgOKQpHaZWg4qSt5i+l3C9nti/cZ0fonlKQkftgymBKViqgd6Z1YvPgPevfux7RpU/nmmxFqxxEihRR9QmSTe/fu4e5ejRIlinP06CHMzAxv/NfDu88Y3mo24U+j8F73BZXq6u/01YqicG2rH4enrCIhOp5qnzenWr8W3Nz9t0HMsAdp12rL27kvxnmsifLdSczfx1ASEjB2cMS6bmPy1G2CuVs5KfREtkoIvEXU0X1EH91H4oN7YGSMZaVqWL/fgjw1GxB96rDBdB9+eWbgml+3JTE6nuMzNqBL1FHr67a492iCkbH+Trlw7/ojRrSaQ1xMPP/b/BWlqxZTO1KWUxSFTp26smHDRo4dO2TQE7UJ/SJFnxDZQKfT0bRpc/z8/Dlzxs8g1/J5cOsJwz6YRUxUHP/b9BVlqruqHemNRT0KxXfCcu4cOE/Byu/RxLsbDiULqx0rS6W3cPpzRrZ5sa7bBOt6TTAvXRGNkf6+yRSGQVEUEu7cSG4BPLwH7eNgNKZmKDotJP3b5doQF4CPehTGwYnLub3/HAUrudJ4anfyuenv81HwnacMbzmbyNBovt84iPI1i6sdKcuFhobi7l4Nc3Nzzpzxw9raWu1IQkjRJ0R2+P77//Htt2P544+F9OzZQ+04We7+zccMbTGLhPhEftwymJLuRdWO9EYUReHy2mMcmbaGJK2OWkPaU6lbI73+ZD0jd/u0QxfyNM12I9u8FFu4CY2JzOUlciYlKYm4y+d46D0MJS4uzX5DXABeURSub/fn0OSVJETH4fFFK6r2bY6xqX6OGXtyP5RhH8wi5FEE0zYOokLtEmpHynKHDx+hYcMm9OjRjcWLF6odR4hXFn2G9y5HCBWcPHmSceMm0KnTJ/To0V3tOFnu3vVHDGk+k8QELdO3fa23BV9E0FM2fzaH/WOXkb9cUTpvHkflnvrdleplilZL9Alfgr8blm7BB5AUGS4Fn8jRNEZGWJavnG4rNSQvAB+5bxtJcbHZnOzd0Wg0lGrlQdftEyjhWYWTczaz5uNpPLkUqHa0N+LobM+MnUPJ52THqPZzOX/shtqRslz9+vX49ttR/PHHUlatWq12HCFeSVr6hHhLkZGRVK5cHa1Wy9mzp8mbN6/akbJU4NWHDG85G51Wx/TtX/Neef2bolpJSuL88oMcn7ERgLojP6J8p3oG1aUxMTiIiL1biDqwHV1YCMb5CpAUG40SE53mWENsJRGGKfDzD9NfAN7YGHQ6NFZ5sK7viW3TtpiXKJ39Ad+hm3vOcHDSCmJDoqjatxk1BrbC2Ez/ZtB9FhzOsJazeHI/jO/XD6RSPcMa+pCYmEiDBo24fPkKZ8+eplgxwxvDKPSHdO8U4h3q2fMzli3z4eDBfdSrV0/tOFnq7pVghn0wG0VRmLH9a1zL6d8Yk4igp+wb/Sf3T13DpV45Gn33KTaFDWNqeCUxgeiTh4jYvYm4C3+DkTFW1etg27QtllVqEnV0X5oxfYY4HiorJd1ehXJ2AsQEgVURNO6TMHqvk95dw1CkNy5VY25Ovv7fYFagEBF7NhN9bD9KQgJmJUpj27Qt1vU9U60jqc/iwqI5Mm0NVzacwMGtMJ4/9MSxnIvasTIt5GE4w1rN5nFgCN7rBlK5QSm1I2WpW7duUblyddzdK+Hru0+WcRCqkaJPiHdkxYqVdO3ajQkTxjFx4ni142SpO5eDGfbBLDQaDTN2fK13ay4pisKltUc5MnUNaDTUH/0xZTvWMYiZKRMfPyRy9wYi9m4lKSIMkwJO2DRtg03jlpg4pF7s+uXZO/V55sN3Len2KpRTA0H3QpdBIwuoNAGjQg1AFw+KNvkr6fl33b/bNMagMUn+bmSS/G8j0+TvxmZgkoek4P1wZgwkvTBOzdgSTY2fpfDLwH/9H9ZFRRB1aDeRezaTcPcmGgtLrN9vjl2LjzArZhgTiNw5eJ79Y/4iLjQSjy9aUe3zFhiZ6FdhEfIoguGtZvPwzlO8135BlYb6vdTPy3x8lvPppz2YPHki48aNUTuOyKWk6BPiHQgMDKRSpaqUL1+Ogwf3Y2JAY6RuX7zP8JazMTYxZvr2r3EpXUjtSJkS/Tic/eP+4q7veZxrlKLJ992xLZJf7VhvRVEU4s6fJnz7OmL8jwBgVb0eti3aY1nJw6C6qmbkTVvIlCQdxD2CmPsQ9xgl/inEP4N/vitx//wccia5eFODkSkUbIjGPD+Y5wOL/GCeH415PrBwBMvCYOmExvj1l4HJbS2KiqIQf/0SEbs2En1kL0piAhYVqmDXsiNWHvXQGOv3c3RcWDQHv1vJ9a1+FKhYjKb/64lDCf36MC70cQQjWs3h/q0nTFkzgGqNy6odKUt9+ml3Vq5czZEjvtSqVUvtOCIXkqJPiCyWlJREkybN8Pc/zblzf/Pee++pHSnL3DwfxIjWczAxNWbGjiEUdSuodqRMubbNj4OTVqCNS6TO8A5U+rShXhdESbHRRPruImLHOhKD7mBkY4etZxtsm3fAxFG/ivG3kW4r3D8tZJqirSHyFkTdQom6CzFBKDEPkou82PsQ+xAUXdqTGpmnFFeY54OH+zO8vlH9lWBs8UIL3ovfjZO/lKQXWv8SX2gN1EJSPIo2GuXYZxnfSYeqKYUo2rRjMUEDFgXAyhmsnNFYOYNVYchTDI1NCbAujsbM7j8fL0Mu/J7TRYQRuXcrETvXo336CON8BbBt3h5bz7Z6v/D7jR2n8Z24nMSYeGoNbU/lHo316jku7EkkI1rPIejGY75b1Z/qTcupHSnLhIeH4+5eDVNTU1nGQahCij4hstiMGbMYPnwkixf/Tq9ePdWOk2VuX7zPsJazMTUzYcaOIRQpWUDtSK8tNiSKg5NXcGPHaQpWcqXp/3piX1x/i6LEx8GEb11N5P5tKDHRmJUojV3LjuSp2wQjM3O142U73YbSEBuUdofGKLnYepGxFeQp8m9hZPm8SCoMFgX/bUkztkrV3Ve3sQzE3Et7DauiGLe/kjX34zWvoWhjISEE4p5A3BOU2H+K2Jj7KP98J+Y+JIanPo95frAuDmEXQBfzTu+LPlB0OmJOHyNi+1piz/mDiSnW9Zti16Yz5q4l1Y73xqKfhHNg3F/cOXCewh5uNP2+B7ZF9ac3Q/jTKEa2mcPdqw/xXvuFQbX4HTx4iEaNmtKvXx/mz/9F7Tgil5GiT4gsdP78eapXr0XLlh+wfv0agxgjBv+M4WsxE2NTY2buHKpXBd/dwxfZN3opcWHR1PiyNVX7NMvx410yGqcUd/0S4ZtWEH3CFzQarOs0xrZlR8xLlTeY/2uvoiTpIOoWhF9GCbuU/D38IoRfzvA2GveJya1cNsXB2hVM877RY5UdrWNZfQ0lMRKiAyHyFkrUzeTvkTfhkW+Gt9GU/wbsyqLJWw5s3DLVZVSfJQTdIWL7OiIPbEeJj8PS3QO7tl2wrFwDjUajd+NfFUXh8vrjHPZeDYpCg3GdKNOhtt48T4Q/i2J4y9kE3XjM1HWGNcZv5MhR/PjjDLZs2Ujr1q3UjiNyESn6hMgi8fHxeHjU4vHjJ5w/fwZHR8f/vpEeCLz6kKEtZqHRwMydQyhaKue2kF3dcpITMzcRGRyCdSF77EsU4t6RyziUdMJz+mc4ls35awimNyMhJiaYFCiM9kEgRlbW2DRri13LjzHJrz/Fd2YpSVqIuIIScgZCApK/h55LXRBZvwd2ZeHRYdBGpj1JFrdcGcrsnRm2KGpMAOXf7q5GpmBXHo1DFXCokvw9b3k0xobbmqyLjCBi90Yitq9FF/oMU5fiWJSuQOTBnZCQkHKcvsx0G3H/GXtHLeHBqeuUaF6VonXLcHr+TiKDQ7BxcqDW0HaUblNT7ZjpCnsSyfCWs3lw+wnfb/gS9/qGMatnfHw8NWrU5uHDR1y4EGAw7xVEzidFnxBZ5Pmnd1u3bqJVq5Zqx8kSQTceM7T5TJKSlBw/S+fVLSc5MNYHbVxCqu1F65Wl1c8DMLHQjxaLDNceMzIiX48vsWnaGiNL/Zly/nWKGEVRIPouypMT8MwP5dnfEHb+3wLPJA/Yu6NxqAx5K6LJWx5sS6Mxtf73Grl4jFpmvXIMpEt7iLiGEn4JQs+nFN0khiUfZ2QKduWSC8B8Hmgcayb/LjRpx43p82QxSmICUUf2Er55JQl3b6Z7jL6saZmkS+LMoj2cmLURJSn1+zoTCzMaTfHKsYVf6OMIhrWczaPAEL7fMJBKdQ1jHb/nvYI++KAFGzas1ZsWWKHfpOgTIgs876f/+ed9+fXXn9WOkyUe3HrCkOYz0SZombFjSI5fh29po2+JfBCSZrtNYQd6HJiqQqI3c+vDuhnuK77+aDYmeXsZFRdUn4ORnRvKkxMoT0/AkxPJM2jCPwVe5eQC73nrko0bGqNXd8nV5wJDDZl5vJKL8jsQcgblny9CAiAhNPkAM3vIXwNN/lrJRWC+6ij3thpEIa4oCrc/ymCNVY2G4uuOZG+gt7C47khinkak2Z7TnyNDHoYzrOVsntwP5X+bBlG+Vgm1I2WJmTNnMWzYSBYu/I3evV8xiZMQWUSKPiHeUnh4OJUqVcXc3JwzZ/zIk0d/WmEyEnznKUObzyQuNoEZ24dQvIKz2pH+07zS/dPfoYEvr8zP3jBvQBceSvi2NYSt+xPSee7Vl1aFF2XYjfBFeVzRONYCx1po8tdM7k74HwWeUJ+iKBB5A+XJcXh6EuXpyX/HVmqMkyfRSUpMe0M9nCwmo9Z3jYUlRWb9iWnBnP2B2HPzyvSH9N7W6cFz5LPgcIZ+MJOQhxH8sOUrynro/6zYSUlJNG3anFOn/Dh79jQlShhGMStyrlcVffozx68Q2czHZwWuriUwMjLD2dmFoKAgli37wyAKvod3nzHsg1nExiQwfevXelHw3dobABl0j7FxcsjeMJmkffqIp4tmE/j5R4St+xOzEmXA1DTVMRpzc+y9MihqcyBFl5BcDLyi4DOqvxyjDjcwbncRozqLMHLri8a+khR8ekKj0aCxdcOoRHeMav6McSt/jDoGYdRwPZpyw9Iv+ABi7qFo05k5NAez9+qPxvylcYxGRigJ8dwb2JnHc74j4d5tdcJlQkbPhea2VuT0D/nzOdkxffsQ8jra8E3bn7hy+o7akd6akZERS5YswsTEhA8+aEOxYsUxMjLD1bUEPj4r1I4nchlp6RMiHT4+K+jXrz8xMf++cTExMWHJksV4eXVRMdnbexwUwtDmM4kMi+HHrV9TqoqL2pFeSRufyNH/reW8z0FsnPMR8yQCXcK/bzZz8niVxEcPCFv3J5G+OyBJwfr9ZuTt8ClmRVz1bqZAACXyJsqDPSjBu+HxkQzWkvuHHrb2iMx5ZSuvkRnkr4WmcFM0Tp7J4zRz+Jim9P4mLctVJnzLSiJ2b0KJj8OqZgPsP+mF+Xs5c8KR9MY9a4w0KEkKxZu609i7OxZ5c/YHl4+DQhjaYhaRodF68Rr1Or74YhC//pq6pdXKyooFC+br/XsKkbNI904hMsnVtQR37wam2V6smAt37qQ/4D8n27vqFIsmbOLxvRCMTYwwMTVm1u5hlK5aTO1orxR68yE7h/zOs6v3qdyrKbWHtufGrtMps3fm1JnpEh8HE7Z2KZEHtqMxMsamSWvs2nfFtEDOnSQnPYo2Bh4dQgneg/Jgd/JSCgDWJdA4NUFTqCFJsU/hzDd6P65LZF664zmNLKBUfzQaBSV4f/JkPQCWhdA4eaIp3AwKNUJjpl8LpOsiwgjftoaIbWtJion6p/j7DPP3ct6kIy/OcGzj5EDNr9sSGxLF8RkbsMpvS7PpvSlcPWevUfjoXghDms0gIiQayzzmhD6OwLGIA70ntaNppxpqx8u0YsWKExiY9gMSfX1PIXIuKfqEyCQjI7N0u8JoNBqSkhLSuUXOtXfVKWYO9CE+9t/cpmYmDJ/fLce+eD5ff+rQdysxsTCj6bQeuDasqHas/5T4ODi5ZW//tuRiz7MteTt8ikk+/ZmuW4kOQgnainJ/e3JrXlJ88sQsBd9HU7gZGqemaGxSj0uRCVZyr//63SsxwSjBeyB4L0rwvuQZQjXGyZPCFG6BpkgbNHal1bsDmaSLjiRi6xrCt6xKLv5qvZ9c/OnBQu+Pzt9l99CFRAQ9pcagNlT7vAVGxjl3lM+auXuZP2pdqm3mlmYM/dkrx752ZcSQ3lOInO2Nij6NRnML6KAoytl3GS4rSNEnspohtfR1KTOGx/fSznhZoKgDK654q5Do1RKiYvGdsJxrW/1wrlEKz+m9sC6Ys1sFEh8//KfY2woaI2w925L3w256UewpigJh51GCtqEEbYXQgOQdtqXQFG6e3DWvQF00xhaq5hT6T0nSwjN/lAe7UR7s+vf/mo0bmiKt0BRpk7xEhB6M+dRFRxK+ZTXhW1ehxESTp1ZD8n7SK8cXf2mfXz/DumBetWOlS99eu17FkN5TiJztTYu+JKCWoiin3mW4rCBFn8hqs2f/xJAhw1Jt09f+903yDEh3u0YDe6N+zeY0r6Zvn0RrQ54StmYJEfu2ABpsPduQt0O3HLWgenotMZpiHeHJMZSgzShB2yD6LqCB/DX/efPdGo1tzhyzJAyHEnP/nw8btsCjQ6BowcIRjfM/BWChRmiMzXN0S7IuKoLwrasJ37o6ufir0xj7Ln0wc865XecVReHKhuMcnJyze1I0tR6Q3iTHOfK167+kN0+Aubk5ixb9rnfvKUTOJkWfEJnwfIrl48dPkC+fAw8eBOPiUhRv7yl69+QcF5NA+yLDSIzXptmXkz4tVRSFc8sOcPSHdXox5kQXGUHYhr+I2L4WRafFtmlb8n7UPUcVe5DBmCuNMZhYQ2I4GJlDocbJRZ7zB2gsC6oXVuRqSkJ48rjRoK3JEwUlRoCpHeStAM/84MUucDlwzKguMoLwLSuTi7+EBGwafYD9J70wcSykdrQMvThmuspnntQa2h5j05zTyppRS1++wnlZff17FRK9HR+fFYwZM5a7dwMxNjYmf/783Lx51SBmBBc5x9sUfTUVRfF7l+GyghR9IivNnTuPr74aoveLqSbEJTL241/5+8AVjE2M0CbqUvblpHER8ZGx7B/zJzd3ncG1USWaTuuRY2eXS4qNIXzbGsI3LicpNhrrBs2w79Qb00I5c8kL3YbSEBuUdoexJZpavyV33zS1zv5gQryCokuAh/tR7m1AueVDugvP5dDZYXVhoYSu/5OInRsAsG3RAfuPumNslzO7qGvjEzny/RourDhEoSrFaTG7L9aFckbW9MajA+R3zsuCY2Owy6+/z12+vgdp1KgpX375BXPnzlE7jjAgb1P07QSevsY1FEVRerx5xLcjRZ/IKtevX8fdvRqNGjVk69ZNOX6K8YwkJmiZ2HUBJ3acZ+Rv3TE2NWbRhE08CQrJUTOgPb4YyM7BC4h8EELtYe2p8plnjnjMX566PW+nPiix0YStXYouPBSrGvVx6NIXs2I5b6Hd5DF651Bur0C5MjeDozQYd43K1lxCvAnd8ow/ADJqugsc66DR5Lwu4NonDwld/UfyDL5m5ti17UzeNp2JPn0sRy7Vcm2bHwfG/oWxmQmeP35GsQbl1Y4E/Dvz9PPXriadPVg3dz8upQsxY/vXWOe1UjviGxs8eAg//TSPfft207hxI7XjCAPxNkXfQyD+Na6hKIpS/M0jvh0p+kRW0Ol01K/fkCtXrnLhQgCFCxdWO9Ib0Wl1TOm5mEMb/ubrOV1o06eB2pHSUBSFiysPc8h7NZYO1rSY1QenajmjO2fkod08/XUaSnzapz6LClVx8Poci9IVVEj2akrMfZQ7K1Fur4Dwy2BkmtyVUxeX9uAc2koi/s/eXYdHcXUPHP/ObNwgISG4OxS3UqQ4bfEKEqy4u1sI7i7BpQQrTnGKu7u7hJCEBIjLzn3/yFvakAQim2yA+3me3/N7mZ2592xbdufsvXOO9KG4ewEqgADrbCg5mqDkaJYqq4CGP3+C/7olBJ08iGJhiYgIB/2/uy4Uc3McuwxOFYmf/0Mv9vRazOu7Lyjd+QfK9qiLapJ6tnv+48ze64xs4k7eEtmYsqMnljafZ5Gp4OBgihcvTXh4OFevXsTOzs7YIUlfAPlMnyTFw5Qp0xg4cDCrV6/ExaW5scNJFE3TmNZ1NXv+OEXnCT/za88axg4phvDAUA65enDvr3Nkq1iImlN+x9LB1thhvfe0U2MifV7FOK6mSUv2ZX+lipXIf4iId4inWxGP18KrY4CIaoidsxlKtkYIzwMxn+lLhc9DSVJcYn0uVWcJpaaj6Myi/tv3OghCA4eSKDmaouT4FcUidT1fG/bgNp5Du0QlfR8wcXIm28LNRogqpoiQcI6NXc/NjSfIXDYftaa1wzp9GmOHFcPx7Zdxa7GYYhXzMn5zN8wsTI0dUqKcOnWKihW/p23bNixevNDY4UhfAJn0SdIn3Lhxg5Ily1K37k9s3Lg+Vd3Yx5cQgvkD/2Tz/EO0GvoTrYfVNXZIMfjeecGeXot4+8Sbcr3qU6pjbRQ1dW3Netj4u9hfUBRybTqessHEQggB3scQD1Yinm2Luhm2zfP/m90mKLbRN12k5sqHkhQfn+wFGOKFeLIxapXb/3LUCnemOqi5W0Om2iiqifGC/4+HP1ckrnKUqeGz5b9ubTnFkVFrMLOxpNbUtmT5toCxQ4ph/5ozTOywggo/FcXVoyMmqagITUIMHjyUSZOmsGvXDn74oY6xw5E+czLpk6SPiIiIoHz573j27DnXr18mffrU9QtxfK0c9xerxu+kcdeqdJ38a6pLXO9sO8Ohkasxs7Wk1rR2ZCmXurZiRfr54r9uCQEHdsT6urF/jRfBnohHHogHqyDwIZimQcnxG0pOF0hXOtX9+5YkYxBvbyEerYkqABP6CiycUXK5oORqhWKX16ixxbWLQDEzJ/O05amuzcPruy/Y02sxbx6/onyfBpTsUDvVfc5sXXiYOX3XU71JWQYvaY2ayn5EjI+wsDBKlSqHv78/169fxt4+dRTSkT5PH0v6Pr+/HZJkYBMmTOLixUu4u8/7bBO+jXP+ZtX4ndRp+S1dJv2Sqr6Y9eGRHBmzjv0Dl5P+mxw02TIsVSV8WlgofuuW8qxbEwIO78ayeDkwM4t2jmJujr1L5xSPTWgRiGfb0R/+GW1bfsSVUVErHd8uQW10H7XMTBTHMqnq37ckGZOSpiBq8TGoDe+iVt4AjmUQt2ah/VUc/f5aaA//QEQGGSU2e5fOKObm0Q/qdIDgea+W+C6ahv7dG2OEFqt0+TLz68bB5KlTilPTtrKrmzthASGfvjAFNez0PW1H1efv9WeZ03c9cS1kpGbm5uasWrUMb29vevbsbexwpC9YnCt9nxO50icl1uXLlylT5luaNPmV1atXGTucRNm18gTTuq6mcqOSDF/ZDl0qamYe6OXPnt6L8br0kOK/1+Dbfo1STR8oIQSBR/fht9od/WtvrL+tikPLzphmyBKjemdKV9gTQc8Q95chHqyAUG+wzIiSq0XUaoWt0WpmSdJnSYR4Ra3+PVgFAffA1A4lZ3OUvO1R0hRM0Vhi+2yxKloG/w3LeLdvG6qlFfa//Y7dDz+jmKSObalCCK6sPMjJKZuwzZyOH+Z0xjF/6mlTI4Rg8YitrJ+xj2b9atN+dENjh5Qobm5jGDVqNFu2bKRhwwbGDkf6TCVqe+fnRCZ9UmJERERQtuy3vHzpxc2bV3FwcDB2SAl2eNMFxrZeSunqBRm9oTNm5sZ7mP3OjjOcnr6NgJd+2GZ0IF/9stz88wSRIeFUH9+KPD+UMlpsHwq9d5PXS2cSdvcGZrnyk65tTywLFU/RGD58TomirqgW6dDuLQbPPVEnZaqDmqcdZKyRap5LkqTPlRACfE4h7i9FPN0c1fA9fUWUPO1RsjZAPN1i1Odfw58+5PWKOYRcPotp5myka9MDq1IVUmz+T/E8f489vRcTHhhK1dEuoBDtM7983wbkr1fOKLEJIZjZay1/LT1Ge7eGNOtf2yhxJMU/9yReXq+4cePKZ3lPIhmfTPokKRZjx45nxAjXz/ZXtX/KVhcok4NJ23piYWX26YuSyZ0dZzg03IPI0OiV6azTp6HBit445M5opMiii3ztg99qdwKP7EGX1gF7l07YVv0xxYvJxFqR8J8S9BZOKLnboORpi2KdLUXjkqSvhQj1QTz8A3FvKQQ9BlM7iAwGEfnvSUaodCuEIPjCSfyWzyHi5TMsS5Qn3e89MMuSI8Vi+Jgg77fs6b2Ylxfuo+hUhF57/5qJhRlVx7oYLfHT6zUmtV/B3xvO0XNGUxp0rGKUOJLin91HzZs3ZeXK5cYOR/oMyaRPkj5w/fp1SpYsS+PGjVi3zsPY4STY1RP3GFR/TlSD2t19sEljadR4VlYdSoCnX4zjNhnsaXNkghEiik4LD+Pt9nW82bQKodeTpl4T7H9uhWoVd+Pn5BRn7zGzdKiN7qPojJfAS9LXRAgNXh5AO9YsVfW0FBERvN29iTcblqOFhmD3Q2Psm7RFZ2P8Xm76CD1LyvUlIihmL1PbTA60PjTeCFFFiYzQ49psIad3X2PwkjbUbGacBDQpRo4cxZgx4/jrr2389NOPxg5H+szIQi6S9B+RkZG0bduBtGnTMmfOTGOHk2D3rzxj+C/zcc7mwKRtPYye8AEEvIyZ8AEEvvJP4UhiCjp3nOe9WuC/ZhGWxcuSdfZq0rXsYpSET2iRiKdb42g2DYT7yYRPklKQoqgomWqBPmYCA0DwM0RkyhcvUUxNSVu/KVnnrcO2Rl3e7d7Es+7NeHdgB0LTPj1AMtKZ6ogIjv2fV1zfBSnFxFTHyD/aU6xyPqZ0XsXpPdeMGk9iDBs2hMKFC9OpU1fevn1r7HCkL4hM+qSvzvTpMzl37jxz587CycnJ2OEkyIsH3gxuOBdrO0sm7+hJWqfU0dTc0t4m1uO2GY33TEKE13O8xg3g1YRBKKZmZBw1iwyDJmCaIUuKxyLC/dFuzkDbXgTtuEtUH7HYWKV8bJIk8dG/e9q2/GhXRiGCPVMwoCi6NPY4dR5I5ilLMc2UDd/5E/Ec0omw+7dSPJb/iuuz3Zif+f8wtzRjzPrO5CqSGTeXxVw7ed/YISWIubk5y5cv5uXLl/TvP9DY4UhfEJn0SV+V27dvM3LkKBo1asivv/5i7HAS5PXLtwyqPxu9Xs+k7T1Jn8X4X65CCM4v2EWIfyB80DbAxMKM8n1T/llJLSwUv7WLed6rJSE3LuHQqhtZpq3Asmisux2SlQh4gHauD9qWfIjLw8EmB2qltVBuIeg+WKHVWaIUc0vxGCVJIurv3od/J1ULKDQAnL5F3JiKtq0g2ok2CL9LKR6fec58ZBo3H6cew4n08eLFoA74uE9GH2CclaDyfRtgYhFzV4JdNkf04RFGiCg6aztLJmzpTvqs9gz7ZT4Prj03dkgJUqZMGQYM6MeSJcvYv/+AscORvhDymT7pq6HX66lU6Xvu3LnLjRtXyJAhg7FDircA/yD61J6B1xNfpu7qTYFSOYwdEuFBofw9ZCUP9l4iX72yZPk2P+fm7jRaJTchBMFnj/F62SwifbywqVQTh9bdMHFI+dVc4XsO7dZMeLYNVNOoJur5u6LYF3t/zofVO1O6UqAkSdF97O+kCHyEuOOOeLASIgPAuQpqwd6QsWaK98nUggLxW7+Ud7s2oVpZ49CiM7bV66LoUrYdzn8rNttksMexYBYeH7xGhhK5+GF2J6zTp0nReGLz6ulrelafiqbXmPV3fzLl/Hx294SGhlK8eGlCQ0O5du0StrapY2ePlLrJQi6SBMycOYs+ffrzxx8raNHCxdjhxFtocDgD683i7sWnjN/cjZJVCxg7JN488WZXN3f8H7ykwsCfKd6mulEbhEd4Pcd3yQxCLp7GNFsuHDv0xbJwiRSNQQgNXuyJSvZ8ToBp2qg+YPk7o1imjuqlkiQljQh/G9VD8848CHkJaQujFOiFkv3XFH8eN/zJA3wXTyf05mXMcxfAsfMAzHMb9/vh/u4LHBiyEjMbS36c24kMxY3fV/TxrZf0rjUNmzSWzD7QH4cMxk9G4+vkyZNUrPg9Xbp0Yt68OcYOR/oMyKRP+urdv3+fokVLUr16NbZv32LUBCUhIiP0jPhtAecP3GTEH+2p3LCksUPiydEb7Ou3FFSFOjPak7WC8Zob69I5YZ6vMCHnT4DOBIem7VO8qbHQhyEer0PcmgXv7oBVVpQC3VFyt0Yxlb/MStKXSOjDEU82RP29f3sTLDOhFOgW1WrFNOUqbAohCDq2n9cr5qJ/549dncaYZs/N240rojWAt61cK8Vi8r39nF3d3Al89YbvXZtR6NfvUmzuuNw694j+P80iU05HZuzti01aK2OHFG99+/ZnxoxZHDp0gO+///zaUEgpSyZ90ldN0zSqVq3BlStXuXHjCpkzZzZ2SPGiaRoT26/k7/Vn6TvXhZ9+r2jUeIQQXFq2n5NTtuCYPzM/zu2MXVbHFI0h4Og+fBdMRIRFrxxnnrcQzgPHY5Iu5bbuiMigqF/8b836/y/+36AU7I2S/WcU1TTF4pAkyXiEEPByX9QK/6ujYJoGJV/nqO3cFin3+agFBeK3ZhHvdm+K8Zpibo5jl8EpmviFvglib98lPDtxi6Itq1Jx8C+oJim7/fRD5w/cZNgv81NFb9uECA4OplixUmiaxtWrF7G2Nk6rIenzIFs2SF81d/eFHD16jOnTp3w2CZ8QgvkDN/L3+rO0d2to9IQvMiyCv4es5OTkzeSuVYKf1w5I8YQPwN/DPUbCB6B/8zrFEj4R/gbt+mS0bYUQFweDbV7UqttQfziFmrOpTPgk6SuiKApKptroqu9GrX0MMnyPuDEJbXshtIuDEcEvUyQO1doGxw590aWNWeBLhIXh7+GeInH8wyKtNfUW96D47zW4+schdnSYQ+iboBSN4UOlaxRiyNLfuXHqIaNbLiYyQm/UeOLLysqKpUsX8fDhQ4YNG2HscKTPmEz6pC/a06dPGTRoKLVq1eT339sYO5x4WzN1D1sWHOKXHtVp2i/lfp2NTZDPW7a0ms7tLacp26MudWa2x9TKPMXjEJpGpM+rWF+L9PU26Fzao/XotxZAv8YG/dYCUQUeQn3QLruibSuIuOoG6Uqh1vwbXY3dKBlrfDZbhiVJSh5KupLoKq1B/ek8SpZ6iDvzo5K/s70QgY+B2D9bDEn/NvbeqHF9diYnVadScfAvVBvfihfn7vPnbxPxe5AySXBcvv+5FL1mNuXMnutM67aaz2W3W+XKlejWrQuzZ8/l9OnTxg5H+kzJ7Z3SF0sIQd26DTh8+Ag3blwhR44cxg4pXnavPMnUrn9Qo2lZBi1ujaoa77cZ7xtP2dV1AaFvg6gxqQ15ahvnmcLwF0/wXTCJ0JtXYn3dxMmZbAs3G2Qu7dF6xNluoP9PQ2ZFF/V/WgRkbYhauD+KQ3GDzCdJ0pdJBD5C3JyOeLgahB7SlQW/i6D9Z7eCzhKl7DyDVe592qlx7AmeTkemsfOxyF/EIPMk1MuLD9jV3R19WAS1p7cnexXjxPGPVeP/YuW4nTTrV5v2oxsaNZb4CggIoHDhYtjZ2XHx4lnMzD6P7alSypLbO6Wv0vr1G9i1azfjxo3+bBK+k7uuMr2HB2VqFGKAeyujJnz3dp1nc/MpoMDPawcYJeETERH4b1zJ8z6tCX/yEJsa9eCDLzrF3Bx7l86Gm/OKa/SED6Ju2BQT1J8uoKu0WiZ8kiR9kmKTE7XsHNT6N1DydQHf09ETPgB9SNRnjoHYu3RGMf9gJ4aJCaqVNZ5DO+O7eDpaSMpvs8xYMje/bRyCXVYn/uo8j0vL9ht1la3lkJ+o264Sa6ftZfP8g0aLIyFsbW1ZsGAuN27cYNKkKcYOR/oMyZU+6Yv0+vVrChb8hhw5snPq1HF0Kdy/KCEOrD/LUtdteD/zAwUy5nBk8elhWNpYGCUeoWmcnfMX5+bvIkOJXPw4tzNWjilXje4fofdu4jt/IuFPHmBdoRrp2vXGxD5dtOqdyVGZTr8mrofkFXTNAw02jyRJX5eU+myJ7TPSusx3+HlEFXrRpUuPU8f+WJWuYLA54ysiOIwDg1fyYO9FCjQqz/duLpiYG+c5aL1eY3SLxZzYcYX6nSpzauc1fJ774ZTFgXZuDajRpKxR4vqU5s1bsmnTZi5fPk/BgilbPVtK/WT1Tumr07r176xZs44LF85QtGhRY4cTpwPrzzK9mwdhIeHvj5lZmNJvfgujfOFEhIRzYNByHuy9RMHGFfjerRk6s5T9QtZCgvFbu5h3O/9EZ++IY8d+WJetlOzzipCXiJszEHfmA7F8LlplRdfwdrLHIUnSl0m/tQAEP4v5gs4K9YdTKHZ5kj2G0DvX8Zk/kYhnj7CuWAPHtr3RpbVP9nn/S2ga5+bv4uycv6J+WJzXGat0Kf/DIkB4aAQdvx3Hs7vRt8SaW5rRd55Lqkz8vL29KVjwGwoUyM+xY4eNuiNISn3k9k7pq7Jv335WrVrNoEEDUnXCB7DUdVu0hA+ivoSWum5L8VgCX71hs8tUHuy7zHeDf6Ha+JYpnvAFXzzN894teffXBuxqNyLrbI9kT/hEqA/axcFo24sg7rqDUwVQP9gepbNEKeaWrHFIkvRlU4q5gc7yg4MmICLRdpZAO9XxfcGX5GKRvwhZpi7Hvml7gk4f4VnP5gQc2pWiWy0VVaVs97rUmdUB31vP+PPXSby++yLF5v8vMwtTQoJiVoQOCwk3yvdwfKRPn54ZM6Zy8uQp3N0XGjsc6TMiV/qkL0pQUBBFihTH3Nycy5fPY2FhnC2S8VXdukusxxUFDgQuSLE4fG4+5a/O8wkPCKHWtHbkrJayybI+8B2vl88m8NBuTDNnx6nrICwKFkvWOUX4W8Tt2Yjbc0EfjJKjGUqRQSi2uaOKuVxxheDnYJUFpZibwQotSJL09Yrts0XJUCVql8H9JSD0KLl/RykyEMUyY7LGEv78MT7zJxF2+yqWxcvh1GUgJk4ZknXOD726+pidXecTERxOnZkdyF65cIrOD1DDpgux3Qqn9PdwQgghqF37R06dOs3Nm1fJmjWrsUOSUgm5vVP6avTrN4Dp02dy9OghKlUybm+7TwkLCadBpn5EhEfGeC19VgfW3h6XInE8PHCZff2XYZHWmrru3XAskCVF5v1H0Nlj+C6cgv7tG9I2ciHtr21QzZKvJYSIDEbcdUfcnA7h/ijZGqN8MxwlTf5km1OSJOlTRLAn4vokxIMVoJqi5OuEUqgvinm65JtT03i3dwt+fywARSFd6+7Y1qyfoi1oAl76sbPLAl7feU6lYb9RtEXVFJsboFmBYVHP1H8gJb+HE+PRo0cUKVKcatWqsn37Ftk2SALk9k7pK3Hu3DlmzpxN584dU33Cp9drjG2zjIiISEzMTKK9Zm5pRju3BskegxCCi0v2sav7QtLlzcSvfw5O0YRPH/AW7xmjeDVxMDo7ezJPWoyDS6dkS/iEPhzt7qKobZyXR0C6Mqh1jqNW/EMmfJIkGZ1ilQm17CzUupdQsjZA3JqFtr0I2rUJiIiA5JlTVUnzw89kmbEK8zwF8XWfjJdbbyK8U66fnm1GBxp79CPH999wdMx6joxeixaZco3T27k1wNzyg6rQikLLIT+mWAyJkTNnTsaMGcVff+1kw4Y/jR2O9BmQK33SFyEiIoLSpcvh6/uamzevkiZNGmOHFCchBLP7rGP74qN0n/obtg7WLHXdlqJVw/ThkRwetYZbm06S54dS1JjYGhOLlOv5E3T6ML4Lp6IPfIf9L21I27glimnyPD8ohIZ4vB5xdQwEPQGn71CLjUJJn/KV6yRJkuJLvLmBdnUsPN8O5o4ohQeg5O2AokumH8aEIGDfNl6vnAuAQ8su2NVuhJJChUI0vcapaVu4tHQ/WSsWos7MDpjbWn76QgP4p4q2z3M/7NLZ8M4viNLVCzL2z66YmKbe6t96vZ5vv63I48dPuHXrGunSJd+qsPR5kNs7pS/e+PETGTZsBNu2baZ+/XrGDuej1kzZw9JR2/itd006jWucInPe2XGG09O3EfDSDxvntJhaW+D/wIsyXX+kbI+6Kfalrn/rj+/i6QSdPIhZznw4dR+Kec68yTafeHkA7fII8L8K9sVQi7lBxhpyG4wkSZ8N8foC2uWR8OowWGdHKeaKkv1XFCV5PrcjfbzwWTCJkMtnsShcAqdugzHNkHK7QG7+eYLDozxIm8OZQr9+x5WVBwl46YdtRgfK921A/nrlkj2GnSuOM72bB3Vafkv/BS1T9XfGlStXKF26PC4uzVixYpmxw5GMTCZ90hft7t27FC1akvr167Fhw1pjh/NR+9ecYWKHFVT7tTRDlv2eIqWW7+w4w6HhHkSGRq8SWqRZZb4f1TzZ5/9H0Okj+LhPRgsOxP63tqRt6IJiYvLpCxNB+F2KSva8DqXITZIkSVJyi/EjVvExKBmrJ89cQhBwcCevl88GvR6HVt2wq90wxX4gfH76Dn91nkfkB9WtTSzMqDrWJUUSvxVjd/DHhF20HPIjbYan7h+Thw0bwfjxE9m3bzc1a9YwdjiSEcmkT/piCSGoVq0mly9f4data2TIkLKVxxLi/N+3GNp4Lt9UyMOErd0xS6GGtCurDiXAM+ZD6raZHGh9aHyyz68PfMfrpTMJPLIXs5z5SN9zBGbZcyXLXCLwMeKKG+LJBjBPh1J4YLJuh5IkSUpJ/25XHw1BTyFDtajkz6F4sswX6euNz/yJhFw+g2XR0jh1H4qJo3OyzPWh5ZUGEeT9NsbxlPruEkIwtetq9qw6Sd+5Lvz0e+qtFRAaGkrRoiXR6/Vcv34ZS8uU2RYrpT6ykIv0xfHwWEuOHLlRVTMOHz5C48aNUnXCd//KM9xcFpEtf0bc1nVOsYQPoiqjJeS4IQVfOsPz3q0IPHaAtL+1JfOkxcmS8IkwP7QLg9D+KoF4vgOlUH/UetdQC3SXCZ8kSV8MRVFRczZDrXsZpeRE8LuMtuc7tJNtEUGxNH5PIhPH9GQYMQ3HTgMIvXuDZ71bplhfvyCfmAkfpMx3F0QVc+kzuzllahZiZq+1nN5zLUXmTQwLCwvc3efx8OFDMmTIgqqakSNHbjw8UvfuJyllJc/eKklKRh4ea+nYsTPBwcHvj61du45q1ari4tLMiJHF7tXT1wxpNBdrO0smbOmGTZqU+wVOCIG5rSVh70JivGab0cGgcwUc3Ye/hzuRvt7o0jlimjEbodcuYJolBxkGT8A8T0GDzBOtz5VlZnCuBJ57IOItSq6WUe0XrDIZZC5JkqTUSNGZoxTogcjVEnFzOuLOPMSzbSgFeiKss8P1iQbrM6ooCna1G2JZrAw+c8fhM2ccQaePYFm8HG+3ribS1xsTx/TYu3TGtnItg71H24wOse5Ssclgb7A5PsXEVIfr6g70rTODMS2XMG1PHwqUypFi8yfEy5de6HQ63r17B8CTJ0/p2LEzQKq8N5JSntzeKX12cuTIzZMnT2Mcz549G48fPzBCRHF75xdErxpTee31ltkH+pOjUMolI1qknqNj1nN93VEUVUVo2vvXDP1cRMDRffgumIgIC4t23LLUtzgPGGewNgzao/WIs91A/0ESm6YQaoVlKPbfGGQeSZKkz4kIeoa44op4vD7mizpLlLLzkpT4vZ9Hr+ftzj/x+2M+6KO3VVDMzXHsMthgiV9cz6M7FsxCY4/+mFlbGGSe+PB79Y6e1SYTEhjG7IMDyJw7fYrNHV+f072RlHzk9k7pi/L0aexbWOI6bizhoRGMaLKAl498GbO+c4omfOGBoezsMp/r645SskNtqk9shW0mB1Cinocw9IPw/h7uMRI+gIinDw3ad09ccY2Z8AGEv5MJnyRJXy3FOitqhWVgHksyog+J+uw0xDw6HWnrN0VnlzbGayIsDH8Pd4PMA5C/XjmqjnWJ9t1VoPG3vL7ryZYW0wh89cZgc32Kg7MdE7b2QBOCIY3m8sYnefomJsXncm8kGU+Kb+9UFKUOMAvQAUuEEBPjOO9nYCNQRgghl/Gk97JmzRLrh1i2bFmNEE3sNE1jYocVXD/5gOEr21GsUr4Umzvw1Rv+6jSP13dfUHWMC4V/qwRAgQblk23OSJ9XsR/39TbYHCL4JQTH8eUV8sJg80iSJH22wnxiPx7XZ2ci6d/E/lydIT/zISrx+/AHyrx1SrGn92I2/jaJeou7ky5fZoPOGZeseZ0Z+2dX+v84k+G/LWDart4xmrobU7ZsWWNd6UtN90aScaXoSp+iKDpgHvADUAhopihKoVjOswV6AWdSMj7p81CmTJkYx6ysrBg3bqwRoond4hFbObL5Ih3HNqLqL7GusicLv/uebGo6mbdPvKnr3u19wpdchKbxZse6OF83cUz6FhihD0O7MRXtr2Jxn2SVcj2kJEmSUq2PfBZqFwYhwmMvjpJQcX22KxaWiIjwWF8zlOxVitB4TX+EprGp+VSen7mTrPP9V+FyuRi2vC23zz1mQrvl6PXapy9KIePGjcXKyiraMZ1Ol6rujSTjSuntnWWB+0KIh0KIcGAd0CCW88YAk4DQlAxOSv3u3r3Ljh1/8e235cmePRuKopA9ezYWLXJPNQ8qb198hA0z91O/Q2V+610zxeb1PH+PTc2moo+IpNHqfmSvXDhZ54v09cZrdB/8ls/BLFd+MIv+i6dibo69S+dEjy+EQDzfibazdNTWpAxVocRE0H1QCEdniVLMLdHzSJIkfSmUYm4xPyNVC0hfGXFnHtqOomj3VyA0fewDxJO9S2cU8w+27qsqIiSYFwM7EP4keZ8hcyqYlV/WD8LGOS3b283h7s5zyTrff1WsX5wuE3/m2LbLLBy6OcXm/RQXl2YsWuT+/t4oTRo79Ho96dM7GTs0KZVI0UIuiqL8AtQRQrT//59bAuWEEN3/c05JYJgQ4mdFUQ4D/T+1vVMWcvk6CCGoXr0Wly5dTrU9+U7tvsbI3xZQplZhxqzvjM5ElyLz3t99gX0DlmOXJR31l/TALotjss4XePwAvgunIvSRpPu9J7Y16hF4bP/76p1JreQm3t5GuzAQvP4GuwKopSa/b0IcrXqnASrTSZIkfUni+owUfpfQLgwAn1NgXxy19FQUp28TPc9/Kzb/85mvs7LBZ9549EGBOLToTJq6vyVrQ/fQt0Hs6uaO57l7VBjYmBJta6IoSrLN919z+29gy4JDdJ/6G426VE2RORPin959mqZx7dol2bvvK5FqmrN/KulTFEUFDgJthBCPP5b0KYrSEegIkC1btlJPnjxJoXchGcuqVX/QunVbFi6cT8eOHYwdTgx3Lz2hT63pZM3nzIy9fbG0SZnKYpdXHOD4hI1kLJmbnxZ0xSKtdbLNpQUF4rt4GoFH92GetxDpe7timtFwWytF+BvEtXGIuwvBxAblm2Eo+TqiqCnX11CSJOlLJYRAPPkTcWkYhHiiZP8NpcRYFCvDPRenf+OPz4KJBJ87jsU3pUjfcwQm6ZJvtUkfHsH+gSu4v/sCRVtWpeKQX1F1yb+RTa/XcHNZxMm/ruK2rhPf1f3IIwhGcvDgIapXr8WwYUMYO3a0scORUkBqSvq+BUYJIWr//89DAIQQE/7/5zTAAyDw/5dkAPyA+h9b7ZMrfV8+X19fChQoQv78+Th27DBqMv5ymBivnr6m+/eTMTU3Ye6hgThkSJPscwpN4/jEjVxZeZDctUtQc0pbTJKx6Xvonet4zxhFpK839r+1Ie3PrVB0hqkFJYSGePgH4vJICHuNkud3lKIjUSzkthRJkiRDE5FBiBvTELdmgqJDKTIIpUBPFJ1hCpMIIQg4sIPXy2ejmJji1G0I1uUqG2TsWOfTNE5M2czlZQfIVbM4taa2xcQi+YushAaH0++HGTy68YLpe/pSoHSOZJ8zoVq3/p21a9dz6dI5ChdO3sc+JONLTUmfCXAXqA68AM4BzYUQN+I4/zBye6cEtG3bnj/+8ODSpXMUKVLE2OFEE+AfRM8aU3n98i2z/x5AjoIZk33OyLAI9g9YzoO9FynWuhoVB/+SbFtohF7Pm02r8N+wHBPH9KTvMwqL/Ib7dyD8LqGd6wuvz4Jj+agtRw4lDDa+JEmSFDsR+Bjt4mB4vgPs8qGWmvp+K70hhHs+xXvGKMIf3MG2VgPS/d4T1Tz5dsFcWfk3xyZsJEPxnPw0vyuWDjbJNtc//L3f0aPqZEKCwplzaACZcqauHyt9fHwoUKAIBQsW4OjRQ6nuR3PJsFJNnz4hRCTQHdgL3AI2CCFuKIoyWlGU+ikZi/T5OHbsOMuXr6R//76pLuELD4vAtfkiPB/44La2U4okfGHvgtnebjYP9l6k4pBfqDQ0+Z6ZiPTx4qVrD/zXLcHmu2pkmbbCYAmfCPdHO9cXbW9lCHyIUn4has39MuGTJElKIYpNDnSV16F+vxm0SLRD9dGOt0QEG6YNjlmmbGQev5A0DZsTsG8bLwa0I+zRXYOMHZtiratTZ1YHfG48ZVPzKbx78TrZ5vqHfXo7Jmzpjj5Sz9DG83jnF5TscyaEk5MTU6ZM5MSJk6xYsdLY4UhGlKIrfclFrvR9uSIiIihZsgwBAYHcvHk1RjliYxJCMKnDSvavPcPgJW2o2cxwzc7jEvjKnx3t5+L/yIsak9qQ76eY7SsMNteJv/FdMBkhNBw79se2Sm2DjCuEhnjkgbg0HML9UPJ2RCk6AsUsrUHGlyRJkhJO6EMRt2Yhbkz+/5bPwSj5uxtsy2fwlXP4zB6DPuAd6Vp2we6nX5PtB0vP8/fZ2WU+OnNT6i/pgWOB5G/rc/XEPQbWnU2BMjmYvKMnZsn4uEVCaZpG5cpVuX37Dnfu3CBdunTGDklKJqlmpU+SEmr27Llcv36D2bNnpKqED+CPCTvZv/YMbUbUS5GEz/+BF5uaRv1yWW9xj2RL+LSQYHzmTcB72khMM2cjy7Tlhkv4/K+g7a+JON0ZbHOj1jmOWnqaTPgkSZKMTNFZoBYZhPrTRchQFXF5BNru8givQwYZ36pYGbLMWIVViXK8Xj4br3EDiIyjyXtSZSqdh8Ye/VBUhc0tpvHibPKtLv6j6Hd5GbioFddO3Gdql9WkpkUVVVWZP38Ob968YejQ4cYORzISudInpVrPnz+nQIEiVKtWle3btxg7nGj2rz3DxPYrqOVSnoELWyV7iWivyw/Z0WkeOhMd9RZ3x6lQtmSZJ+zRXbynjSTi5XPS/twK+9/aopgkvViLiAhAXB2LuDsfzByiqsXldCGqYK8kSZKU2ogXe6JaPAQ+jKryWXICimXSWyUJIQjYu5XXK2ajWlrj1GsEVsWT54fTAE8/trebzbvnvtSa1o7ctZL/8QGPybtZ5radVkN/ovWwusk+X0L06zeAGTNmcfLkUcqXL2/scKRkkGoKuSQXmfR9mX79tSk7d+7ixo0r5MyZ09jhvHf1+D0G1ptNofK5mLStB6ZmhqlgGZfHh6+xp9cirJ3TUn9pT9JkNfxD4kII3u3ZjN+Kuai2dqTvNRLLb0oZZFyeb0c73z+qPHiedijF3VDM7A0QtSRJkpSchD40qsrnzamgs4z6/M7TziA/2IU/ecir6SOIePaYtI1bYN+0g0F+ZPxQiH8gO7vMx+vyI6qMbMo3zasYfI7/EkIwpfMf7F19KsUe/YivgIAAChQogrNzes6ePYVJMvzzloxLbu+UPjt79uxl48ZNDB8+NFUlfM/vezOy2UIy5EiH25qOyZ7w3dx0kp1dF2CfOyM/rx2QLAmfPvAdryYP5fXi6Vh8U4os01YkKuHTHq1Hv7UA+jU2Uf//1jy0I7+iHWsO5g6oNQ+ilp0tEz5JkqTPhKKzQC06DPXHM+BQHHGuN9q+alFb9T/4zNcerU/Q2GbZc5F58lJsa9bnzebVeI7oRoS3l8Hfg6W9DQ2W9ybH90U44raWM7O2J+vWS0VR6DOnOcUr52Nqlz+4evxess2VULa2tsycOY1Lly6zYIG7scORUphc6ZNSndDQUIoUKY6JiQlXrlzA3Nzc2CEB8PZ1ID2qTibwbQhzDw0kU67kK8sshODCor2cnr6VrN8V5IfZnTBLhmbvobev4T3dlcg3r3Fo0YU0dRNXCVR7tB5xthvoQ6K/oJqjFBuFkr8riip/UZQkSfpcCSEQj9cjLg2GUF9QVBD6f0/QWaKUnYeas0mCxw48fgCfBZNQVB1O3QZjXf57wwX+f1qknkMjPbi16SSFfqvI967NUE10Bp/nHwH+QfSoNoU3PgHMOTSQrHmdk22uhBBCUKfOT5w+fYbbt6+TMWPyVx2XUo5c6ZM+K5MmTeHBgwfMmzc71SR84WERjGzqjvdzf8as75y8CZ+mcXzCn5yevpV8dctQ172bwRM+oWn4b1qF5/BuoNORaZw7aes3TXQlNXHFNWbCB2DmgFqwp0z4JEmSPnOKoqDmbIpa9xKYWEVP+AD0IVHfBYlgU7EGWaYtxzRDZl5NHobvomlo4WEGiPpfqomOauNaUrrzD9zccJw9vRcTGRZh0Dn+y9bemvGbu6HqVIb9PI+3voHJNldCKIrC3LmzCA0NpX//gcYOR0pBcqVPSlUePHhA4cLFaNSoIWvXrjZqLAfWn2Wp6za8n/lhbmVGWHA4w1a0pdqvydcmQR+h5+DQVdzZfoZirapRcYhhmq4HHN2Hv4c7kb7e6BzSoVrbEfH0IdbfVcep80BU66Q1sNWvsY7jFQVd89TxRSdJkiQZhn6NDRDb/WPSPvNFRAR+Hgt5u30tZtnzYF2lNgG7NxLp642JY3rsXTpjW7lWosf/x5VVBzk2bgOZy+Xnp/mdMbOxTPKYcblx+gH9fpyJc7Z0hIWE4/vCH6csDrRza0CNJmWTbd5PcXV1Y/TosRw8uJ+qVb83WhySYclCLtJnQQjBjz/W48SJk9y+fZ1MmTIZLZYD688yvZsHYSHh74/pTHQMXNTK4B/Sd3ac4fT0bQR4+qEzN0UfFkH53vUp1fkHg1QFDTi6D98FExFh0X81talRD6cug5I8h3i2A+24S8xffQGssqJreDtJ40uSJEmpi35rAQh+FvMFU3vUX54mudBL8PmTvJo+EhEafQeJYm6OY5fBBkn87mw/w99DVpIufxYK/VKBi4v3EfDSD9uMDpTv24D89QxXgGV2v3Vscz8S7Zi5pRl957kYLfELCQmhSJHimJmZceXKBczMDNOPUTIuub1T+ixs2bKVPXv2MmbMKKMmfABLXbdFS/gA9JF6lrpuM+g8d3ac4dBwDwI8o3oV6cMiUE102GZJZ7A2EP4e7jESPoDQK2eTNIcIfon+WHO0Y03BMiOoH2zF1VmiFHNL9PiSJElS6qQUcwPdh6tjKkT4ox2ojXibtB/7rEpXQLWKuYNEhIXh72GYAiT565fjp/ldeX33BUdGr4v6HhZRbR4ODffgzo4zBpkH4NTOazGOhYWEG/yeIiEsLS2ZO3cWt2/fZvr0mUaLQ0o5MumTUoXAwEB69epLsWJF6datq7HDwed57A1j4zqeWKenbyMyNHpyqUXqOT3dcF8EkT6vYj/u652o8YTQ0O4tQdtZEjz3ohRzQ61/HaXcArDKCihglTXRD/RLkiRJqZuaswlK2XnRPvMpvzjqe+DtLbTd36JdG4/QJ/65PL3/61iPJ/a7KzbZqxTBIo1VjJ2qkaHhBv0eTql7ioT64Yc6NG7ciNGjx/LkyROjxiIlP1ldQUoVxowZx/Pnz9mwYU2q6BvjkCENr1++jXHcKYuDQef5Z4UvxvGXhvkiCLl5GVQVNC3GayaO6RM8nnh3F+1Md/A5Ac5VUMvMRrHLA4CSswnIJE+SJOmroMbxmS8y1UZcHIS4Ng7xZBNqufkoTgnfKmnimD7WHy1VW7tExRuX4NcBsR431PcwRN07eD+LOZ6h7ykSY+bMaezdu49evfqydesmY4cjJSO50icZ3e3bt5kxYxa//96ab7/91tjhEPg2JNZtj+aWZrRza2CweXxuPUNRY99eaZsxaV8E/1TnfDmyJzq7tGBqGu11xdwce5fOCRgvEu3mNLRd5eHNDZRyC1Cr7Xyf8EmSJEkSgGLpjPrdCtQqmyAyEG1/dbQLAxCRQQkax96lM8qHFbwVBe3dW3wXT0NEhMd+YQLF9X2b1O/h/2rn1gBzy5jPzP3cvZrB5kisrFmzMmLEMLZt286ePXuNHY6UjGTSJxmVEIKePftgZWXFxInjjR0O+kg9Y1ouxt/7Hc0H1CF9VgcUBdJndTDoA9cvL9xnS8vpmNlaojOLnpCZWJhRvm/ik0t9wDu8xg/E32Mh1uWrkHXuOpy6DcXEyRkUBRMn5wQ9CC/8r6Lt+x5xeSRkroNa9wJq7lYGe+ZQkiRJ+vIomeug/nQeJW8HxJ35aDvLIrwOxft628q1cOwyOPp3V/dhpKnfjHe7N/NiaGciXnkmOc7yfRtgYhEzIctR7Zskj/2PGk3K0neey/t7CocMabCwMmPXihMEvo2l3VEK6927J/ny5aNnzz6ExVIDQPoyyOqdklFt3ryFn3/+jdmzZ9CjR3djh8PsvuvYtvAI/ea34MfW3yXLHE+O3mB3D3dsMjjQYHkvPC/ci6reaYCqYWH3b/FqynAi/V+T7vce2NVpnOjkTOjDEDcmI25MBXN71NIzULI1StRYkiRJ0tdLeB9HO9MNAu6j5P4dpcQ4FLM0iR4v6OwxfGaPBUUhfa+RWJWukKT43lfRfumHjXNaTCzNefPYm6qjm1P4t0pJGjsulw7fZlCDOZSsWoBxG7uiS8ZG8fGxd+8+6tT5iQkTxjF4sOzf97mSLRukVCk4OJiCBb8hbdo0XLhw1ujP8m11P8ycfuv5rXdNOo1rnCxz3N99gX0DluGQJxP1l/bAKp1hnk0QQhCwbyu+S2dhktaB9APGYZG3YOLH8z2HdqYLvL2FkqMZSqlJKObpDBKrJEmS9PURkSGIa2MRt2eDRQbUsrNRMv+Q6PEivF7waspwwh/dJe3PrbBv2h5FZ5jEKSIknD09F/Lk6A0qDGhMyfZJbxERm50rjjO9mweNunxP96nGfya+ceNf2bt3H3fu3CBLlizGDkdKBNmyQUqVJk6czNOnT5k7d7bRE74ze68zb8AGKtQtRvvRDZNljpt/nmBv3yU4f5ODRqv6GCzh00JD8Jk9Bt+FU7H8phSZpy1PdMInIkPQLg5B218NIt6hVtmEWmGJTPgkSZKkJFFMLFFLjEOtdRjM7dGO/IJ24ndEWOxVOj/FNENmMo13x7ZGPd5sWsXL0X3Qv/E3SKymlmb8OK8LeX8szckpmzk1fSvJsUjyU5uK/NqzBlsWHGbrwsMGHz+hpk+fgqZp9O8/yNihSMlArvRJRvHgwQMKFy7GL780ZvXqVUaN5dGNF/SsPpVMuZyYub8fltbmn74ogS4t28+JSZvIVqkwP8zphGksD3QnRviLJ7yaPIyI54+xb9KOtL+0RlET91uO8DmDdroTBNxDydMOpcRYFFPDVkmTJEmSJKEPR9ycirg+CcwdUMvOQclSN9HjBfy9E9/FU1Ft7HDuNwaLgkUNEqem1zg8ag03NxynSLPKVBnZNNHfsXHR6zVGNVvI6d3XGLepG2VrFTbo+Ak1evRYXF3dOHhwP1Wrfm/UWKSEk9s7pVSnfv1GHDp0mDt3bhi1Ebu/9zu6VZlEZLieeUcH4ZTZ3qDjCyE4M2sH5xfsIk+dktSc0hadmWFWNQNPHsJn3ngUUzPS93bFqnjiisxEbbkZg7g9Bywzo5ZfgJKhqkFilCRJkqS4CP+rUT82+l9FydEEpdRUFPPEVc0Me3Q36pl2Hy8cWnUlTd0mBik4JoTg1NQtXFyyj3z1ylJ9Qmt0poZ9/i4kMJReNabh9cSX2X8PIEch490XhYSEULhwMaysrLh06RymH1T/llI3ub1TSlV27tzFjh1/4eo6wqgJX3hYBKOaL+KNTwBj/uxi+IRP0zg6dj3nF+yi0K/fUWt6e4MkfEIfyevlc/CeOhyzrDnJMnVZ4hM+37Noeyogbs1Cyf076k/nZMInSZIkpQjFvihqrSMoRYYinmxC21ka8XxnosYyz5mPzFOWYlXqO/yWz8F72ki0kIS1iYg1RkWhwoDGfNuvIXd3nGV3j4VEhhqmXcQ/LG0sGLuxC+aWZgz/dT5vfQMNOn6CYrG0ZObMady4cYN58+YbLQ7J8ORKn5SiQkNDKVKkOKamply5cgEzM8Nsc0woIQSTOq5k/5ozjPyjPVUalzLo+FqknoPD/+D2ltOUaFuTCgMTX0XzvyLf+OE9bSShNy5h98PPpGvTAyURv8IJfSji6ljE7VlgmSmqeW7G6kmOT5IkSZISQ/hfQTvVEd5cR8nRFKXUlESt+gkheLt1DX4e7phmyobzoPGYZc5ukBivrTnCkdHryFw2Hz8t6IKZtYVBxv3HrXOP6FN7OgXL5GTyjp6YGmhnUEIJIfjpp/qcOHGSO3dukCFDBqPEISWcXOmTUo3p02fy4MEDZs+eYbSED2D9jP3sX3OG1sPrGjzh04dHsrfvUm5vOU25nvUMlvCF3rnOi/6/E3bvJk69RuLYoW+8Ez7t0Xr0WwugX2ODflNOtO1FELdmoORqHbW6JxM+SZIkyYgU+2KotY+hFBmCeLIRbVcZ9BeH/vvdtbUA2qP1nx5HUUjbyIWMrjPRv3vDi4HtCTp9xCAxftO8CjUntcHz/D22tZ1F6NukryT+V8EyORno3oqrx+8xq/faZCkeEx+KojBr1nRCQ0MZPHioUWKQDE+u9Ekp5unTpxQoUIQff/yBjRs//cGdXE7uvMLIJgup8nNJhq9oZ9Am45Gh4ezuEVXmueKQXyjepkaSxxRCELB3K77LZmKSLj3OA8djnjNvvK/XHq1HnO0G+v82gFWgYB90JcYkOT5JkiRJMiThdxntaBMIfh79BZ0lStl5qDnj194g0vcVr6YMJ+zeTdI2bol9sw4GaevwYP8l9vZZgkOeTDRY1hNLB9skj/lfy0dvZ/Wk3XSZ+Au/9DDej7JDhw5nwoRJnDhxhAoVktYLUUoZspCLlCr8+mtTdu7cxa1b18ie3TBbLRLqwbXn9Kw+lewFMjBjb1/MDVRFEyA8MJSdXebz4tw9gzV01cLC8F00hcBDu7Es+S3pe49EZ5Owipr6rQUg+FnMF6yyomt4O8kxSpIkSZKh6bfkh5DnMV9I4HeXiAjHd8lMAvZvw7JYGdL3GYXOLm2S43ty7Aa7u7tjmykdDVb0wsbZcHUBNE1jdMslnNh+mbF/dqVcnSIGGzshgoKCKFCgCI6O6Th//gw6A/VBlJKP3N4pGd3Bg4fYuHETQ4cONlrC5/fqHcN/XYBNGktGr+9s0IQv9G0Q29rOwvPCfWpO+d0gCV/EK088h3Ym8NBu0v7WlgxDJyc44ROaPvaED2L+gipJkiRJqUXIi9iPx/WdFgfF1AynLgNx7DaE0JtXeNG/LWEPkv6DZ/ZKham3pCeBr96wufk03j3zTfKY/1BVlUGLWpPrmyyMbbOUxzc9DTZ2QlhbWzNt2mQuX77C4sVLjBKDZDhypU9KdpGRkZQoUZqgoGBu3ryKhYVhH3yOj/CwCPr/OJP7V54xc38/8pUwXOIZ/Pod29vOxu+BF3VmtidXjeJJH/PyWbynu4IQpO81EqvSCd9WIQIeRpXC9jkZ+wlypU+SJElKpeLcpaKYoNY6hJKuZILHDLt/i1eTh6F/649jp/7YVvspyXG+uvqY7e1nY2JhRsPlvbHPbbiiJz4v/OlaaSLmlqbMOzKYNI42Bhs7voQQVK1ag+vXb3Dv3i3s7Q1b6VwyLLnSJxnVokWLuX79BtOmTTZKwieEYHp3D26cfsigRa0NmvAFvvJnS4vpvHn8irruXZOc8AkheLPFA6+x/dA5OJJ58pIEJ3xCCLR7S9F2l4c3NyBPB1A/+Oeus0Qp5pakWCVJkiQpuSjF3EBnGf2gagamtmj7vke7OhahRSRoTPM8Bck8dRkWBYviM3c8vounIyIjkxSnc9EcNFrVFy1Sz+YW0/C9bbhdNE6Z7RmzoQuvvd4xymUREeFJizUx/inq4u/vj5ubrAPwOZMrfVKy8vPzI2/eghQvXowDB/YatGhKfK2dtpclI7fSenhdWg1J+q96/3j33JetrWcS4h9IvUXdyFQ6/sVV/ivg6D78PdyJ9HmFYm6OCAvDukI1nLoNQbW0StBYIsQL7UxX8NwLGaqhlndHscocVczlimvUlk6rLCjF3OL9ILwkSZIkGUNs311K5tqICwMQj9aAQ0nUCktR7PIlaFyhj8TvD3febl+LRaFiWH9Xg7dbVxPp642JY3rsXTpjW7lWgsb0f/SKbW1mEhEcRv2lPXAumjNB13/MwQ3nGPf7Mn5oXYF+81oY5V6qS5duLF68lKtXL1KoUKEUn1+KH1nIRTKaHj16MX++O5cvn+ebb75J8flP7rrKyN/cDV6p883jV2xtPZOIkDDqL+mJc9EciRon4Og+fBdMRISF/XtQp8Ox+zDsqtRO0Fji2Q60s90hMhCl+DiUfJ2M8sUgSZIkSclNPN2Kdq4nRAajlBiPkrdDgr/zAo/uw3vuOPhgtU8xN8exy+AEJ37vnvuytc1MQvwCqbeoO5lK50nQ9R/zT0XPrpN/5edu1Qw2bnz5+vqSN29BypQpzd69u+T9RSolt3dKRnH9+nUWLFhIly6djJLwPb7pyfjfl5G3eFYGLGhlsA8ov/uebG4xjciwCBqt6pvohA/A38M9esIHoNfzZs3CeI8hIgLQTndGO9YUrLKg1jmBmr+z/ECWJEmSvlhKtoaoP56B9BUR5/ugHW6ECHmZoDFsKtdCZxuzQJoIC8Pfwz3BMdllcaSxRz+sndKwvf1snp8y3HPzrYfXpWL94rgP3sj5AzcNNm58OTo64uY2kv37D7Bjx18pPr+UdDLpkwzOw2Mt2bPn4ptvSiCEoEiRlC81/PZ1IMN/W4CFlTmj13fGwsowlTp9bz9nc4vpADRe3RfHAlkSPZYQgkifV7G+FunrHb8xfE6h7SqPeOSBUnhg1MPtaQokOiZJkiRJ+lwolhlRv9+CUno6eB9H21UO8WxbgsbQv/GP9Xh8v4c/ZONsT2OPfthldWRHp3k8OXojUeN8SFVVBi9uTfaCmRjTegnP7sV+/5CcunTpTMGCBenQoTPZs+dCVc3IkSM3Hh5rUzwWKeFk0icZlIfHWjp27MzTp1EVtzRNo1+/ASn6gRAZoWd0i8X4vnjD6PWdcMpsmEpTr64+Zkur6ZhYmNJ4dT8c8mRK9FhaWCjeM0bF+bqJY/qPXi/04WhXRqEdqAWKglpjH2oxVxSd4dpQSJIkSVJqpygKar5OqHVOgHV2tGPN0U51RES8i9f1cX3f6hwcEx2TlaMdjVb2xSF3BnZ2nc/DA5cTPdZ/WdpYMPbPLuhMdIz4bQGBb4INMm58mZqaUr9+Xby9vXn69BlCCJ48eUrHjp1l4vcZkM/0SQaVI0dunjx5GuN49uzZePz4QYrEMKvPWrYvOsqgRa2p5VI+0ePc2XGG09O3EfDSD0sHWyKCQrFyTEPDlb2xy5L4L4NIHy+8Jg4h/PE9rCtUI+jcMQgPf//6p54lEO/uop1sC36XUHK1Qik1GcXUNtHxSJIkSdKXQGgRiGsTEDengFVW1ArLUJw+fh8Q67P1gGqbhoyjZmKeM2FFYv4r7F0w29vPwefGEwo3rcTjg9cIeOmHbUYHyvdtQP565RI17tXj9+j/00xKVi3AuE3d0OlSbg0nNdznSXGTz/RJKeafFb74Hje0HUuOsn3RUX7rVSPJCd+h4R4EePqBgJDXAUSGRVCsTbUkJXyht67wYmB7Il69IMPQyTj3G41T1yGYODmDomDi5BxnwieEQLu/HG33dxD4BLXSWtTyC2TCJ0mSJEmAopqiFhuJWmM/oKAdqIl2dRxCi7vVgW3lWjh2GRztezhtsw4oZuZ4DulM4Im/Ex2PuZ0VDZb1wi6rE9dWH3l/TxHg6ceh4R7c2XEmUeMWrZiXXjObcW7/TRYN25zo+BLD2Pd5UuLJlT7JoLJkyc6LF54xjqfEL0CXj95lYL1ZlKpWkLEbuybpl6+VVYdGfTh/wDaTA60PjU/UmO/2bcN3yXRM02fEefAkzLLEv1+gCPWNqsz5fAc4f4/67RIUq4yJikOSJEmSvnQi4h3iXF/E47XgWC6qtYNN/NsoRL7x49XkYYTdvkraX1pj37Q9ipq4+4oVVYYQ6BXz2cGk3FMAzOm3nq3uhxng3oo6Lb9N9DgJIVf6Uje50ielmJw5Y36gWllZMW7c2GSd1/ORD24tFpE5d3qGrWiX5K0OAS9jJnwfO/4xIjIS30XT8HWfjGXR0mSatDhhCZ/XQbTd5cBzD0qJ8ajVdsiET5IkSZI+QjG1Q62wBKXCcnh7G23Xt2iP1hDfxQ6TtA5kcpuFbY16vNm4klcTB6MFByUqlsBXsReLScw9xX91nfQLJb/Pz8yea7hxOmUSrnHjxmJlFb2HsKWlZbLf50lJJ5M+yWBOnz7N8eMnqFevHtmzZ0NRFLJnz8aiRe64uDRLtnmDA0IZ+Zs7ml5jzIYu2KSxTPKYFmltYj1um9EhQePo3/rz0q037/ZsJk1DFzIMmYzOOn7bMYU+DO3iELSD9cA0DWqtw6gFe6Eo8q+tJEmSJMWHmuM31B9Pg31RxKkOiJO/I8LfxOtaxdQMxy6DSNehL8EXT/NicEciPBO+jTGue4eE3lN8SGeiY8QfHXDKYs/Ipgt59SxpSWR8uLg0Y9Eid7Jnz/b+WOvWrZL1Pk8yDLm9UzIITdP49tuKPHv2nLt3b2JjE3vSZCgH1p9lqes2vJ/5YWZpSkRYJBO39qB09YJJHvv+ngvs7bMEIYD//P0wsTCj6liXeD94Hfb4Pq8mDEL/1g+nrkOwSUCTV/H2DtrJNuB/NarhbInxKCZWn7xOkiRJkqSYhKZH3JyGuDYWLDNFFXlJXyHe14dcv8irKcNB00jfbzRWxcvG+9p/6gREhoZHO563bmlqT2sf73Hi8uT2S7p/Pxk7B2v0kRq+nv44ZXGgnVsDajSJf5wJpWka5ct/x4sXnty9exNra+tkm0uKH7m9U0p2a9eu4+zZc0yYMDZFEr7p3Tzw/v8vWuEhEag6lTe+AUke+96u8+ztuxTnYjmpOqY5tpkcQInad5+QhC/ozFE8h3ZG6PVkGjs/3gmfEALtwUq0PRUh+AVq5Q2oZWbKhE+SJEmSkkBRdahFBqLW/BtUE7S/a6Ndm4DQ9PG63rJISTJPWYqJY3q8xvbn7c4/471VNH+9clQd6/L+nsImoz3OxXJw76/znHffnZS3BUD2Ahn58feKeD15jc8Lf4QA72d+TO/mwYH1Z5M8flxUVWXWrOl4enoyefLUZJtHMgy50iclWXBwMPnzF8bZOT1nz55CTeSDzvHVrMCw9wnff6XP6sDa2+MSPe6dbWc4MHgFGUvloa57N8xsLBI8hhCCN5tW4b9mEeZ5C+E8aAImn+j1oz1aj7jiCsHPQGcJ+hBwroL67VL57J4kSZIkGVhUkZc+iMfrIH1FyNIQbs+C4OdglQWlmBtqziaxXquFBOM9azTBZ49hW7M+ju37opiaJjgGLVLPgcErubvjLGV71KNs95+S9J6S694oPpo2dWH79h3cvXuTLFmyJOtc0sfJlT4pWU2bNoPnz58zY8a0ZE/4AHyex75nPa7j8XFz00n2D1pB5rL5qLeoe6ISPi0sDO+ZbvivWYRNldpkHDM3fgnf2W5RCR9EJXyKCeRsKRM+SZIkSUoGUUVelqJ8uxh8z8HF/v//HhYQ/Axxthvao/WxXqtaWuE8cDxpf2lNwP7tvHTrjf7dmwTHoJroqDGpDQUaf8vZOTs4PXN7vFcOY5Mc90bxNWnSeDRNY8iQYck+l5R4MumTksTT05OJEyfz88+NqVSpYorMmS5j2liPO2VJ3APRNzYc4+DQVWStUICf3LthamWe4DEi/Xx4OaIbQccP4NCiC049R6CafXoccXlkVKIX7WAkXHVLcAySJEmSJMWfmrM5mNnHfEEfErUDJw6KquLQvCPpe7sSdu8mLwZ1IPzJw4TPr1OpPq4lhX79jvMLdnFq2tZEJ35x3QMl9t4oIbJnz07fvr1ZvXoNZ88m33ZSKWlk0iclybBhI4iMjGTy5AkpMl9khB5ru5ircOaWZrRza5Dg8a6tOcKhER5kr1yYnxZ0xdTSLMFjhN67xYuB7Ql//hjnQRNI27gFiqJ88joR/AJCnsf+YnAcxyVJkiRJMpzQV7Efj8f3sE3lWmQcOw8RHsaLIR0JOnc8wdMrqkrV0S4UaVaZi4v3cmLypkQlfu3cGmD+wT2MooDLoDoJHisxhgwZhLOzM337DkjSiqWUfGTSJyXaxYsXWbnyD3r16kGuXLlSZE73IRt5ctuLuu0rkT6rA4oStV+97zyXBFeouuZxmCNua8n+/Tf8OK8zJuYJ35MfePwAL0d0RTExJfOEhViXrRSv68TzXWi7ygNxJIdWck+8JEmSJCW7uL5vTdPGK3mxyFuIzJOXYpY5O68mDubN5tUJTnoUVaWKazO+afE9l5cd4MSkhCd+NZqUpe88l/f3RmnT26KoCie2X0Gv1xI0VmLY2toydqwbJ06c5M8/Nyb7fFLCyUIuUqIIIfj+++rcunWbe/dukSZNmmSfc9fKE0zruppfe9ag84SfkzTW1dWHODpmPTmqFuWH2R3QmSUs4RNC4L9+KW82LMeiUDGcB4xDlyaWLSIfXqcPQ1wegbgzD+yLQvamcG1M9C2eOkuUsvPifIhckiRJkiTDeP9sfbRHLVRAQ8nWGKXsHBSztJ8eJywMn3njCTp+AJuqP+DUeSCKacJ2DwkhODZuA1f/OESx1tWoOOTXeO0cisuOpceY2XMNTfvWosOYRokeJ770ej2lSpXl7dt33Lp1DQuLhNdHkJLmY4VcTFI6GOnLsGXLVo4ePcaCBXNTJOG7cfoBs3qtpXT1gnQY0zBJY11ZdZBj4zaQs3ox6szsgM4sYX8NtLAwfOaMJejkQWyq/YRTpwHxqtwlAh6inWgFfpdQ8nVBKTEWRWeBZpnh/9U7P101TJIkSZIkw1FzNkGDaN/DFHVFCfVEXHFDvL6A+t0qFMdY76P/HcfcnPR9RvEmS3b81y0l0usFzgPHx+sH4X8oikKlYb+hKApXVh5EaOL9nxOjXrtKPLj6jHXT95GrSGaqJ2PPPgCdTsf06VOpXr0WM2fOZvDggck6n5QwcqVPSrCwsDAKFSqKpaUlly+fx8QkeX878PF8Q5fvJmBla868I4OwtU9888/LK/7m+IQ/yVWjGLVnJDzhi/Tz5dXEwYQ9uI1Dyy6kadA8Xh/G2uM/EWd7gGqCWm4BStZ6iX0LkiRJkiSlAOFzBu1kGwj2RCk+GqVADxTl009GBZ74G585Y9HZO5JhyCTMsiXsERghBMcnbuTKir/5psX3VB7eJNGJX0R4JAPrzeb2+cfMOtCffCWyJWqchGjQoDGHDh3m3r1bODs7J/t80r9kywbJoObMmcfDhw+ZPn1Ksid84aERuDZ1JzQ4jNHruyQx4TsQlfDVKkHtmR0TnPCFPbobVaHrn4ItDV0++SEsIoPRznRDnGwDaQuj/nBKJnySJEmS9BlQnMqh/nASsvyEuDQU7cgviFCfT15n8111Mo6ZhwgL48XQzgRfPJ2weRWFioN/oXjbGlxbfZijo9clujiKqZkJI/9oT1pHG1ybuuPv/S5R4yTElCkTCQkJYcSIuCugSilPrvRJCeLj40OePAWoWPE7du7cnqxzCSGY3GkV+zxOM3p9Z76rWyzRY11atp8TkzaRu3YJak1rj85U98lrAo7uw9/DnUhfb1RbO7SQYHRpHMgwZCLmOfN9Ov43N6O2c769jVK4P8o3w1DUhBeLkSRJkiTJeIQQiHuLERcHg7kDaoXlKM6fLtwW6fsKrwmDCH/yAJvKtQi5fhH9ax9MHNNj79IZ28q1PjnvySmbubR0P0WaVabKyKYoieyHfPfSE3rVmEaB0jmY8lcvTOJxH5QUffr0Y/bsuVy6dI6iRYsm61zSv+RKn2Qwo0aNJigoiKlTJyX7XFvdD7PP4zSth/2UpITv4tJ9nJi0iTx1SiYo4fNdMJFIn1cgBNq7txAZSdpGLeKV8GkP/0DbWxnCfFGrbkMtNkomfJIkSZL0GVIUBTVfR9Tah8DEBu3gj2jXJyHEx6timjg6k2nsfMxy5CXw8B70vt4gBJE+r/BdMJGAo/s+OW+FAY0p2aE219ce5fCotQgtcZU485XITr95Lbh6/B4LBid/dc2RI4eTNm1a+vUbKFs4pBIy6ZPi7ebNmyxcuJjOnTtSsGDBZJ3r0pE7zB+0kQp1i9Fi8I+JH2fZfk5O3kyeH0pRa1q7eCV8AP4e7oiwsOgHheDt1tUfvU5EBqGd6og43Rkcy0Rt58xYPbHhS5IkSZKUSij2xVDrHEPJ9gvi6mi0Qw0Qod4fvUa1tEL/zj/GcREWhr+H+6fnVBS+7deQUp3qcGP9MY64rUt04lejaVl+7VmDre6H2b3yZKLGiC97e3tcXYdz4MDf7Ny5K1nnkuJHJn1SvA0cOAQbGxtGjRqZrPN4PXnN6JaLyZI3PYMXt0ZN5FaGS8ujet3k+aEUtaa2RTWJ/1aGSJ/Ym7VG+sb94S7e3ETbUxnxaA1KkaGoVf9CscyY4LglSZIkSUqdFFNblArLUMrOA5+TaLu/Rbw6+tFr9K9jfw7wY/cU0eZUFMr3aUDJjrW5vu4oR5LwjF+HMQ0pWbUAs3qv5da5R4kaI766dOlM/vz5GTBgMJGRkck6l/RpMumT4uXgwUPs3LmLYcOG4OjomGzzhAaH49rUHX2kxuh1nbG2s0zUOJdXHODExI1RWzoTmPBFvHwOutjPN3FMH+vx99s5w1+jVtuBWnQYipq8++UlSZIkSUp5iqKg5mmDWuswmNiiHfwparunpo/1/LjuHXRp0iZozm/7Nny/1fOI29pEJX46Ex3DV7bDMVMaXJst5PXLtwkeI75MTU2ZNGk8t2/fZsmSpck2jxQ/MumTPknTNPr3H0j27Nnp0aNbss0jhGBa1z94cO0Fw5a1JWvexJX5jWrLsJHctUtSc2q7BCV8ITcv82JQh6iGqh/03lPMzbF36Rw95ti2c2aomqi4JUmSJEn6fCj230Tf7nm4YazbPe1dOqOYm8c4rg94R+DxA/Gf7/9bPUu2r8X1tUcTXdUzTTob3NZ1JuhtCKNcFhEeFpHgMeKrfv16VK5cCVfX0QQEBCTbPNKnyaRP+iQPjzVcunSZ8ePHYGFhkWzzbJi5n4N/nqeta33K1SmSqDGurIzqwxdVpTP+z/ABBBzew8tRvdClSUuW6Stw6jYUEydnUBRMnJxx7DI4WqUt8fYW2t4qcjunJEmSJH2lYt/ueSzaObaVa+HYZXC0e4p0nQZgkb8I3tNd8d+wPN7Jm6IofNu/ESXa1uTamiMcHZO4xC/3N1kYuLAVN888ZG6/DclWbEVRFKZOnYS3tzeTJ09Nljmk+JEtG6SPCgkJIX/+wjg7p+fMmZOJfr7uU84fuMmQRnOp1LAEI1a1T1QT0iurDnJs3IYEtWWAqBVG/3VLePPnCiyKlMR54Dh0NnYfvUZ7tBZxtieYWqNWWIaSoVqC45UkSZIk6csh/K+hHW8JgQ9QirqiFOr70WbuIiIcn/mTCDyyB5sqdXDqOihqp1F85hKCE5M3cXnZgSQ1cF/iupW1U/fSe1Yz6rWvnODr46t585Zs3bqNe/dukTlz5mSb52snWzZIiTZr1hyePXvG1KmTky3h83zkw9g2S8leMBMD3FslLuH74xDHxm0gV62EJXxaeBjeM0bx5s8V2FavS8YR0z+a8Al9KNrZHohT7cGhBGqdUzLhkyRJkiTp/9s9j6JkbYS44op25DdEmF/c55ua4dRzOPbNOhB4ZA8v3XqjD4jfM3aKovDdwJ8p/ntUA/dj4xK3Wvf7yPqUq12Euf03cP3UgwRfH1/jx49Br9fLhu1GJFf6pDj5+PiQO3d+qlb9nm3bNifLHCFBYfSoOgVfT3/mHx1MplxO8bruzo4znJ6+jYCXfpjbWRH2NphcNYtTe0aHeCd8+ndv8JowmLA713Bo0YU0jVw+mnCKgIdox1uA/5WoX++KuqKoJvGaS5IkSZKkr4MQAnF3IeLSYLDMiPrdHyiOsS6+vBd4/AA+c8Zh4uhMhuFTMc2YJd5zHZ+4kSsr/iZbpUL43X9JoJc/thkdKN+3AfnrlfvkGIFvgulaeSIhgWEsODEEx4xp4zV3Qg0YMIhp02Zw+fJ52bA9mXxspU8mfVKcevToxYIFC7l+/TIFChQw+PhCCMa2WcqRTReZsKU7ZWoWitd1d3ac4dBwDyJDw98fU1SFauNbUrBRhXiNEe75FK+x/dH7+eDUcyQ2FWIWX9EerUdccYXg52DmAPpg0Fmgll+EkiXxvQMlSZIkSfryCd/zaCdaQshLyPYbvDoCIS/AKgtKMTfUnE2inR96+xpeEwYBkGHwBCwKFovfPEKwo8Mcnh67Ge24iYUZVce6xCvxe3TjBd2rTiFn4UxM39MHM3PTT16TUP7+/uTOnZ8yZUqzd6/s3Zcc5PZOKcHu3LmDu/siOnZsnywJH8Cfsw5weOMF2o1qEO+ED+D09G3REj4AoQnOzv4rXteH3LyM5+BOaMFBZHSbE3fCd7YbBD8DBIS/Bn0oFB4iEz5JkiRJkj5JcSyNWucEpCkEjz0g5DkgIPgZ4mw3tEfro51vUeAbMk9chM7WDk/XXvGu7KkoCn73X8Y4Hhkazunp2+I1Rs7CmRm4sBW3zj5ibv8N8bomoezt7Rk5chj79u1n7959yTKHFDeZ9EmxGjx4GJaWlsnWiP3CwVssHrGFyo1K0rRfrU9f8B8BL2PfHx/X8WjnHNnLy1G90aVJS+aJi7DIH3uVUHHFFfQhHx6FO3MSFKskSZIkSV8vxdwBQl/HfEEfEnWv8QHTjFnINGEhFvkKRVX23LgyXs/qBXr5x3o8PvdG/6jSqCTN+tdm57Lj/LXs2KcvSISuXbuQO3duBgwYhF4fe19DKXnIpE+K4dix42zduo1BgwaQPn3sDUWT4uVjX8a0Xkq2/BkY6N4ywYVbLNJYx3rcNqNDnNcIIfD/cwU+s0Zjkb8ImcYvxDTDR6pHBT+L4/jzhIQqSZIkSdLXLuRF7MfjuKfQ2aYho+tMbCrVxH/NInznT0RERn50irjugT52bxSb30fWp0yNQszpu54bZx4m6Nr4MDMzY8KEsVy7dp1Vq/4w+PhS3GTSJ0UjhKB//4FkzpyZPn16GXz80OBwRjVbiNA0Rq/vjKVNwvr+3dlxltC3wShq9ETRxMKM8n0bxHqNiIjAZ+54/NcuxqZKHTKOnI7ONvYKnUJoaDemxB2AVfwerJYkSZIkSQLivncwsULow2J9STE1w6m3K2l/bUPA33/hNbYfWlBgnFOU79sAE4uY7R7Sf5M9QaHqdCpDl7fFKYs9bs0X8fpl/KqJJsQvv/xM+fLlGD7claCgIIOPL8VOJn1SNBs2/MnZs+cYO9YNKysrg44thGB6t9U8uPaCoUvbkjl3wlYR7+06z4GBy8lcJi9Vx7bANpMDKGCbySHOB5X1QQG8HNuPwEO7sG/SDqeew+PsgSPC/aPKK18ZBenKgvpBQqqzRCnmlqCYJUmSJEn6uinF3EBn+cFBE4gMQttfExH0NPbrFAWHZh1w6j6UkBuXeDG0M5E+XrGem79eOaqOdXl/b2ST0Z6MpXLzYO8lzi/ck6B47RysGb2uM0HvQnBrsYiI8I+vMiaUoihMmzYZT09PZsyYZdCxpbjJ6p3Se2FhYRQs+A12drZcuHAWnS5+rQ/ia+Ocv1kweCNtXevjMvCHBF17f+9F9vZZQsYSuai7qDtm1p9eIYz08eLl2P5EeD7FqesQbKvGPafwu4R2rAWEvEApMQElX2fE4w3/Vu+Mo9KWJEmSJEnSp0SrCP7/ewrFxBLtdCdQTFArLEXJFHeNg5BrF3g1aSiKuTkZhk3FPFe+T8+p1zgwcDl3/zpHhQGNKdk+YTUUDm+6wJhWS6jXvhK9ZzVP0LXx8csvTdizZy8PHtzB2dnZ4ON/jWTLBileZs2aTe/e/di7dxe1atU06NiXjtxhYL3ZVPipKK4eHRLU6P3hgcvs6bWI9N/koP6SnpjFY0to2MO7eI3rjxYWSoaB47EsGnd/HO3BSsS5PmCeDrXiahSnT5c2liRJkiRJSirx7j7acRd4cwOlyGCUIkNQ1Nh/dA9/+pCXY/ujBb7Duf8YrEp++8nxtUg9+wcs596u81Qc8ivF21RPUHyLhm9h/Yx99Jvfgh9bf5egaz/l3r17FCpUlE6dOjB37myDjv21kkmf9Elv374ld+78lChRnP37E7YN4FNePfOjS8UJpElnw7wjg7Cyjf9zfI+PXGNXN3ecCmWjwbKemNlYfvKa4IuneDV1BKqNHRmHTcUse65YzxP6UMT5vogHKyFDVdQKy1Es4tccXpIkSZIkyRBEZDDiXC/EozWQsSZqhWVRVT9jEenng9e4gYQ/eYBjp/7Y1az/yfH1EXr29l3Cw32XqDKyKd+4fB/v2PR6jSEN53L1+D1m7u9HgdI54n1tfHTr1oNFi5Zw8+ZV8ubNa9Cxv0ayT5/0SVOmTOP169dMnDjOoOOGh0bg1nwREWGRjF7XKUEJ39MTN9ndfSHp8mWm/pIe8Ur43u3bhtf4QZhmzErmiQvjTvgCn6Dtr4F4sBKl8EDU77fJhE+SJEmSpBSnmFihlF+EUmY2vDqCtqciwu9SrOeaODiRaew8LIuXwXfBJPw8Fn6ypYPOVEftae3IUbUoR0av48afx+Mdm06nMmxFWxwy2DGq+SL8vd8l6L19ysiRwzE3N2f48JjtKyTDkkmfhKenJ9Onz6RZs6aUKlXKYOMKIZjVZx13Lj5h8JI2ZM2XId7XPj9zh51dFpA2pzP1l/bE3O7jRWWEEPh5LMTXfTKWxcqQaexcTBxiT+KE5360PRUh4AFq5Q2oxVzj3EohSZIkSZKU3BRFQc3bDrXGfhB6tH3V0R6sivVc1dKKDEMmYVuzAW82rcJnphsiIvyj4+vMTPhhdgeyVSrMoREe3N56Ot6xpUlnw6g1nXj7OpAxrZeijzRcfz1nZ2f69+/Lhg1/cu7cOYONK8Ukt3dKdOrUheXLV3L79nVy5Yp9ZSwhDqw/y1LXbXg/i2oIWqFuMcas7xzv6z3P32dHhznYZnKg0R99sXSw/ej5IiIcn7njCTy2H9ua9XHs2A9FZxLzPKEhbkxBXB0DaQuhVlyDYpcnYW9OkiRJkiQpGYlQH7QTbeDVYZQ8bVFKTUXRmcc8TwjeblmN32p3LAqXwHnQeHQ2sbek+kdkaDh/dZ7PizN3qDm1Lfl+KhPvuPZ5nGZSx5WUrVWYx7de4vPcD6csDrRza0CNJmUT+jbfCwgIIHfu/BQuXIiDB/cnuH+z9C/5TJ8Up9u3b1OkSHG6du3M7NkzkzzegfVnmd7Ng7CQf39xMrMwpd/8FvH6QPC6/JBtbWdj7ZSGRqv7Yu2UJtbzAo7uw9/DnUifVyimpoiICOxdOpG2cezN3kX4G7ST7cFzN0r231DKzUUxib3JuyRJkiRJkjEJTY+4Ohpxcyo4lEKt5IFinTXWcwOP7sN77nh0dlH3THr/15g4psfepTO2lWNW7IwICWdHhzm8vPiA2jPak6d2yXjHNbD+bC78fSvaMXNLM/rOc0lS4jd37jx69OjNrl07+OGHOoke52uXqp7pUxSljqIodxRFua8oyuBYXu+rKMpNRVGuKoryt6IoCesqKSXI0KEjsLKyYsSIYQYZb6nrtmgJH0Q917fUddsnr/W+/oTt7edglc6Whqt6fzTh810wkUifV0BU83VMTDBxyhB7wud/DW1PJXi5H6XUNJQKy2TCJ0mSJElSqqWoOtTibqiV1kHAPbQ93yG8DsZ6rk3lWqRp0By9ny96P18QgkifV/gumEjA0X0xzje1NKOuezeci+ZgX98lPPr7SrzjenonZp/AsJDweN3nfUzHjh3InTs3gwYNQa833PZR6V8pmvQpiqID5gE/AIWAZoqiFPrgtEtAaSFEUWAjMDklY/yanDp1ii1btjJgQD+cnAxTxOSfLZ0f8nke+/F/+N5+zra2szC3s6Lhyj7YONvHea6/hzsiLCz6wchI/D3cY5yrPV6Ptq8q6INRa+xFzd9ZbhuQJEmSJOmzoGSth1r7KFikRzvUAO3mtFgLtwQdjVl5XYSFxXpvBGBmY0G9xT1wLJiV3b0W8+TI9XjF4/vCP9bjn7rP+xQzMzPGjRvNtWvXWbNmbZLGkmKX0it9ZYH7QoiHQohwYB3Q4L8nCCEOCSGC///H00CWFI7xqyCEYODAITg7O9O3b2+DjWtpE3PPOYBTlthLDwP43fdka5uZmFqa02hlH2wzxX0u8H6FL8ZxX+/3/1toEWgXBiBOtgWHkqh1TqA4lY/HO5AkSZIkSUo9FLu8qLUOo2RtjLg8Eu14C0REQLRz/nsPFJ/jAOa2ltRf2pN0+TKxq8dCnp26/clY4rqf+9h9Xnz9+usvlC5diuHDXQkNDU3yeFJ0KZ30ZQae/efPz/9/LC7tgN3JGtFX6q+/dnL8+AlGjRqBtbVhtjruX3OGkMAwdLro/1mZW5rRzq1BrNe8efyKrW1mopqoNFzZG7usjh+dI+Bw3D0ETRzTAyBCXqEdrIe4Mx8lf1fU6jtRLONfOVSSJEmSJCk1UUxtUL5bgVJiAjzfgbb3e8S7u+9f/+ce6EOqpdVHWzpYpLGm/tKepMnuxM4u8/E8f++jcbRza4C5pVm0YzoTNc77vIRQVZVJkybw9OlT5s9fkOTxpOhSbcsGRVFaAKWBKXG83lFRlPOKopz38fFJ2eA+c5GRkQwePIx8+fLRrl1bg4x57/IzpvfwoFilvPRb0JL0WR1QFEif1SHOh3vfPfNla+uZaJEaDVf0Jm0O5zjHF0Lgv2kVPrPHYJo1J5hF/8BRzM2xd+mM8D0X1Y7h9XmUb5eglpqCopoa5D1KkiRJkiQZi6IoqAV7olbbAWG+aHsqI57tAMDepTOK+Qe7rVQVLTgI3wWTEPrIOMe1tLeh4fLe2GSwZ0fHeby6+ijOc2s0KUvfeS6kzxq1smdhbYY+UsMqjp1eCVWtWlXq1KnNuHETefPmjUHGlKKkaPVORVG+BUYJIWr//89DAIQQEz44rwYwB6gihIh7Xfr/ZPXO+PHwWMuwYcN58uQpAD179mDWrOlJHvedXxBdK00gIlyP+4kh2Kf/eLlggEAvfza7TCPsXTANV/XBqWDsFakAhD4S3yUzCNi7FZvKtXDqNpTAU4eiqnf6er+vUGWd6TnifD+wzIRaeS2KfdEkvzdJkiRJkqTURgQ9RzvWHPwuoBQeiPLNcAKP/x3t3iht805EvnjCm40rsSz5Lc79RqNaxt33OPDV/+/N3gbTcGVvnApl+2QcYSHh9KoxFc+HPsw/NoQseWJfcUyIK1euUKJEGWxtbQkICCBbtqyMGzcWF5dmSR77S5eaqneeA/IqipJTURQzoCmw/b8nKIpSAlgI1I9PwifFj4fHWjp27Pw+4QNYvHgJHh5Je1hWr9cY33YZPi/e4OrRIV4JX5DPW7a2nkmIfyD1l/b8aMKnhYXyavIwAvZuJW3jFjj1Goliaop1Vn8y/3CT7D9fJnOdG1iFz0Cc7QHOVVDrHJMJnyRJkiRJXyzFOgtqzX0ouVsjbkxGO/IzVhme/Xtv9MNNbLK9waF5Rxw7DyTk8hlejuyB/k3shVgAbJztabiyD6bW5mz7fRav7774ZBzmlmaMWtMJ1UTHqGYLCQkK++Q1n3L9+k1UVeXdu3cIIXjy5CkdO3ZO8j3r1y7F+/QpivIjMBPQAcuEEOMURRkNnBdCbFcU5QDwDfDy/5c8FULU/9iYcqXv03LkyB0t4ftH9uzZePz4QaLHXT5mB6sn7qL3rGbUa1/5k+eH+AWwpeV0Ajz9qL+kBxlLxd0cXf/uDV7jBxJ27ybp2vchzQ8/A6A9Wo842w30IdEvyFwXtdIaFFWX6PcjSZIkSZL0OdHuL0ec6w1CD/znvl5niVJ2HmrOJgSdO473tJHoHBzJOGI6phnjrpP45ok3m12mgRA0Wt0P+5xxP37zj/MHbjK44Vy+/7kUw1a0TVKl9OS6Z/0ayObsEqpqFuuDvIqioGnhsVzxaSd3XWXErwuo3eJbBrjH3hT9v0LfBrG19Uz8H3pRb1F3spTPH+e5Ea888RrTl0ifV6TvMwrr8lXev6bfWgCCn8W8yCoruoafrjwlSZIkSZL0JdFvyglhsWyQ+8+9Ueid63iNHwiKQoZhU7HIWzDO8fwevGRLi+mopjoae/QjTdZPt/bymLKbZaO203XSL/zcvXqi30ty3LN+LVLT9k7JSLJli30LZVzHP+XFA28mtl9B3uJZ6TWz6ScTvvDAEHa0n4Pf/Zf8OLfzRxO+sId38RzSGf27N2QcNTNawgfEnvABBD9P6NuQJEmSJEn6/IXFUdTwP/dGFvmLkGmCO6qlFS9Hdif44qk4h3PInZEGy3sRGRbB1tYzCPD8dB++Zv1qU6FuMdyHbubq8Y9XAf0YQ9+zSlFk0veV6N+/f4xjVlZWjBs3NsFjhQSF4dp0IapOZdSaTjFK934oIjiMvzrNw+fmU+rMbE/2yoXjPDf48lk8h3dFMTEh03h3LAoWi/a60IeCLo4WE1aypaMkSZIkSV+huO6BLDNG+6NZpmxkGu+OaebseI0fRMDfO+Mc0rFAFhos7UnY22C2tplBkPfbj4agqiqDFrUmUy5HRrdcgu/LNwl9FwCMGzcWK6voBWfMzc0Tdc8q/UsmfV+JW7duotPpyJQpI4qikD17NhYtck9wJSQhBNO7rebxrZcMX96WDNnTffT8yLAIdnVbwMuLD6g5pS25ahSP89yAw3vwGtcfU+fMZJq4ELMsOaLPHfQcbX9N0AeBYhL9Yp0lSjG3BL0XSZIkSZKkL4FSzA10ljFf0MIR/lejHTKxT0emMXOwLFoKn3nj8f9zRZy9/NIXyU69xT0I9nnHtt9nEuIX+NE4bNJY4ramEyFBYbi5LCYiPO5WEXFxcWnGokXuZM8eVT1UVVXy5s0rq3cmkXym7yvw8OFD8ucvTPv2bVmwYF6Sxto07yDzB/5JW9f6uAz8IdZz7uw4w+np2wjw9ENnboo+LILqE1pRsHGFWM8XQvB26xr8/piPRZGSZBg0AdXaJvo53sfRjreAyFDUCksREYGIK65R2xassqAUc0PN2SRJ702SJEmSJOlzpT1aH+3eiFyt4MFyCPdHKTcfNcdv0c4XERH4zJ9I4JE92NZqgGOHfii62IvhPT91mx0d5+KQJyOFm1XiwoI9BLz0wzajA+X7NiB/vXLRzj+86QJjWi2hQacq9JzeNEnva8aMmfTtO4C//95HtWpVkzTWl04WcvnKtWjRis2bt3L//m0yZcqU6HGunbxPvx9mUK52EdzWdUJVYy4U39lxhkPDPYgM/fdBW9VER/WJrWJ8IAAITeP18tm82/kn1hVrkL7HMBTTf7eLCiEQ9xYhLgwE21yoldahpIn7eUBJkiRJkiQpigjxQjveEnxOohTsjVJsdLQq50II/D3cebN5NVZlK5O+zyjUD5u8/9/jI9fY2WUBQhPwn/zBxMKMqmNdYtznLRi8kY1z/mbw4jbUbB7zHjC+QkNDyZevEBkzZuD06RNJqgz6pZOFXL5i165dY82adfTo0S1JCd/rl28Z3WIxGbKnY9Ci1rEmfACnp2+LlvABaJF6Tk/fFuNcERGO93RX3u38E7u6v5G+t2v0hE8fhjjTFXG+L2SqhVrrsEz4JEmSJEmS4kmxzIBabSdK3o6IWzPRDjdChP1blEVRFBxadCFdu94EnzvGS7fe6APexTpWjirfYG5nFS3hA4gMDY/1Pq/j2EYUq5SXGT09eHA18cX2LCwscHUdztmz59i2bfunL5BiJZO+L9ywYSOxs7Nj0KABiR4jMkLP6FaLCQ4IZdSaTtiktYrz3LiqOwW8jH5cCwrk5Zh+BJ08iEOrbqT7vSfKfxJJEfwS7UBtxMNVKEUGo1Zej2KWJtHvQZIkSZIk6Wuk6MxQy8xAKTsPvI+i7a2CeHMj2jlpfvqV9H3dCLt/C8/hXYj0fRXrWKFvYn+m78P7PACdiY7hK9tjk9YK1+YLCfAPSvR7aN26Ffnz52fYsJHo9fpEj/M1k0nfF+zEiRPs2PEXAwf2x8HBIdHjLBq+hesnH9B3rgu5imSO8zwhBKbWFrG+Zpvx3/kj/XzxHNGN0FtXcOo1krQNm0dbqhc+Z9D2fAdvb6JWWoNadASKIv9TlSRJkiRJSiw1TxvU6nsgMghtX1XEs+irZjbfVSfjiGlEvvbhxeBOhD99GGOM/97Pxee4g7Mdrqs74P3Mj4kdVqJpWqJiNzExYexYN27evMnq1R6JGuNrJ++kv1BCCIYOHYGzszO9evVI9DiHNp5n09y/adTle6o3KfvRc8/O3kFEUCiKLvp/ViYWZpTv2wCA8BdP8BzamQivF2QYOgXbKrWjnavdX4H2d20wsUKtdQgla4NExy5JkiRJkiT9S3Eqj1rnOKQpiHasGdrVsQjxbyJm+U0pMo2ZB0LDc1hXQm5ejnZ9+b4NMLGI3qpLUVXK9a4X55yFy+em66RfOb37Gmun7k107D//3JhSpUri6jqasLCwRI/ztZJJ3xdq7959HD16jBEjhmJtHUdfu094fOslU7uupnD5XHQa//NHz724eC/n5u+i4M8VqD6hFbaZHEAB20wO7x/uDb17A8+hXdBCQ8g0eg5WJf59qFfow9HO9UGc7QbpK6PWPoqSNu5+fpIkSZIkSVLCKVaZUGvsRcnVEnF9AtrRJoiIf5/jM8+Zl0wTFqJLa4+XWx+CTh95/1r+euWoOtbl/X2ema0lQtPwPHc/zrYPAA06VaH6b2VYPnoH5w/cTFzcisKECeN48uQJixYtTtQYXzNZvfMLpGkapUqV5e3bd9y+fR0zs483T49N0LsQulWZROCbENxPDsExY9o4z73mcZgjo9eR98fS1JzaFlUX87eE4IuneDVlOLq0DmQcOQPTjP82ERWh3lGVpbyPx1pZSpIkSZIkSTIsIQTi7kLExYFgmwe18gYUuzzvX9e/e4PXuAGEPbiNY4d+2NVuGOs4p2Zs5YL7Hoq1rkbFIb/GWV0zJCiMHt9PxtfrLe7Hh3yy13NcMVevXovr12/w8OFdbGxsPn3RV0RW7/zK/PnnRi5fvoKb28hEJXxCCKZ0XsWLBz6MWNXuownfrS2nODJ6HTmqFqXG5N9jTfgCDu3Ga/wgTDNlI9N49+gJn99ltD2V4fV5lArLUEuMkwmfJEmSJElSMlMUBTV/Z9Rqf0HY66gCL57737+us0tLRrfZWBYvh+/CKfivXxbral753g0o2rIqV1Ye5OzsHXHOZ2ltzqi1ndAi9bi5LCI8NCJRMY8fPwYfHx9mzpyd4Ou/ZnKl7wsTERFB4cLFMDc34/LlC+jiaLL5Metn7GPR8C10Gv8zv/WqEed59/dcYG+fJWQul5+6C7thYm4KQMDRffh7uBPp641qZY0WFIhl0dI4DxyPavXvVlPtyUbE6c5g7hBVndOhRMLfsCRJkiRJkpQkIvAJ2tHf4O1NlOJjUAr0er9iJyIj8VkwkcBDu7Gt3RDz/N/wZu0iIn29MXFMj71LZ2wq1uDQCA9ubjxBhf6NKNmhdpxznfjrCiObuPNjm+/oN69FouJt1OgXDh48xMOHd0mXLuErhl8qudL3FVm5chX37t1j3LgxiUr4Lh25w5KRW6ncqCS/9qwe53mPj1xjX7+lZCiei5/md4mW8PkumEikzysQAi0oEFQV68q13yd8QmhoV0YhTrQGh+KotY/JhE+SJEmSJMlIFJvsqLUOQpYGiEvDEKfaISJDol4zMcGp+zDSNGpBwN6t+M4d9/4+L9LnFb4LJhJ4/ADfj3Yh70+lOTl1C9c8Dsc513d1i9Gsf212rTjBrpUnEhXv2LFuBAQEMHny1ERd/zWSK31fkLCwMPLmLUjGjBk4ffpEnHuq4+Lj+YbOFcZj52DNvCODsLKNvf3C89N32NFxLg55MtJwZR/MbS3fv/a0U+OoD4IPmDg5k23hZkT4W7ST7cBzN0ru31FKT0fRJXwLqiRJkiRJkmRYQgjEjSmIq27gUBK18joUq3/bdT1uWTvqB/0P/HOfp4/Qs7vHQh4fukr1ia0p2OjbWOfR6zUGN5jDtZP3mf13f/KVyJ7gWFu2bM2mTVt48OAOGTNmTPD1XyK50veVWLRoMc+ePWPs2NEJTvgiwiMZ3WIxYSHhjFrTMc6Ez+vyQ3Z2mU+arI7UX9IzWsIHxJrwAUT6eiPe3UfbVxVe7kcpPQOl7ByZ8EmSJEmSJKUSiqKgFhmIWnkDvLuHtqciwuf0+9e14NgbrEf6egOgM9VRZ1YHslYowMGhq7i/50Ks5+t0KsOWt8XeyRY3l8W880t443ZX1xGEh4czYcKkBF/7NZJJ3xciODiYceMmUqVKZWrUiHtbZlwWDt3EzTMPGbCgJdkLxP5ric+tZ+zoMBcrRzsaLO+NpUP0ikn6t/5gYhLrtdZ5VbS9VSDsNWq1Haj5OiY4MZUkSZIkSZKSn5LlJ9Tah8DEFu3vOmgPVgJg4pg+1vP/e9zE3JQf53UhQ/Fc7Ou/jMdHrsV6TVonW0au7oCv5xsmtFue4MbtefLkoW3bNixcuJinT58m6NqvkUz6vhBz587n1atXjB3rluBk6uCGc2xZcJifu1enSuNSsZ7j/9CL7W1nY2ptToMVvbFOnyba6xHeXngO6wKCDxI/gV1BP9IVvQTWWaL67zlXTuC7kyRJkiRJklKSkqYgap0jkL4S4kxXtAsDsG/eAcXcPMa5NjXqR/uzqZU5dRd1J12+zOzusYgXZ+/GOkfBMjnpNuVXzu67wepJuxMc4/DhQwEYM2Zcgq/92shn+r4A7969I2fOvJQtW4bdu/9K0LWPb3rSrcok8hTPyrRdfTAxjVn85d1zXza7TEMfEUljj/7Y53SO9nr404e8HN0HERZGhqGT0B5vQPfSHZ15KEJTUXUaZG2AWn4RiqnspyJJkiRJkvS5EFpkVHGXO3MhQ1XCQvKje7UCnXko+lAL3t7NQdAzezIMnohl0eiPk4X4BbKl5TQCPP1ouLI3zkVzxhxfCCa2X8Hf688xfnM3ytYqnKD4evXqw7x5C7h16xp58+ZN0nv93Mln+r5wM2bMws/PjzFjRiXouqB3Ibg2X4iljQUjV3WINeEL8n7Ltt9nEREcRoNlvWIkfKG3r+E5vCsAmcbOw8ziNubvFmJiEYqiEJXwKSaQuZ5M+CRJkiRJkj4zimqCWmoSSrkF8OoY5m//vc8zsQzFocQTbAtE8HJsfwJPHox2raWDDfWX9cIynS3b28/B986LmOMrCn3muJCjUCbGt1uO15PXCYpvyJBBmJmZ4eY2Jknv80snk77P3OvXr5k+fSaNGjWkdOlYE/tYCSGY0uUPPB/6MvKP9qTLmCbGOSF+gWxrO4tg33fUW9IDxwJZor0efP4kL0f1Qmeblkzj3THLnhtxxRX0IR9MFglX3RL1/iRJkiRJkiTjU3O3AjMHop7l+ZeihWD/jRfmeQriPW0k7/Zsifa6jXNaGi7vjYm5KdvbzuLN45hF/yyszBi1pmOiGrdnyJCBnj27s2bNOq5fv56o9/Y1kEnfZ27q1OkEBAQwerTrJ889sP4szQoMo4ZNFxpm7sexrZfoMKYRRSvGXAoPDwxhe/vZvH3izU8LupKhWPTl+IBDu/GaOBjTrDnING4Bpun/X/wl+Fnskwc/T/B7kyRJkiRJklKRMJ/Yj4e8IKPrDKxKVcB30VT81y/jv4+Q2WV1pMGK3mh6jW2/zyLA0y/GEFnypGfQ4jbcvfSUIY3nvr9nbVZgGAfWn/1oWAMG9MPW1hZX19FJentfMpn0fca8vLyYPXsuzZo1oUiRIh8998D6s0zv5oH3Mz+EgMC3Iaiqgr2zbYxzI0LC+avTPF7feU6d2R3JUj5/tNffbFuLz5yxWBYpQabRc9CltY9quH75I4mnVZa4X5MkSZIkSZJSv7ju5ywzoZpb4DxoPDZVf8R//VJeL56O0Ovfn+KQOyMNlvUkLCCErb/PJNj3XYxhvqtbjPI/fMPlI3ff37N6P/NjejePjyZ+6dKlo2/f3mzevIULF2JvE/G1k0nfZ2zixMmEhYUxatTIT5671HUbYSHh0Y5pmmDZqO3RjunDI9jd3R3PCw+oOaUtOasWff+aEILXq+bht3Iu1hWqkWHYFFRLa0REANrRJoibUyF9ZVA/6PGns0QpJrd3SpIkSZIkfc6UYm6gs4z5gmqGCH6JojPBqftQ0jR04d2ezXjPGIWI+Pf+06lQNuot7EbQqzds+30WoW9i9ud7cC3m7rCwkHCWum77aGx9+vTCwcGBESNGJfh9fQ1k0veZevbsGQsWLKRNm1bxqlTk8zzmMvqHx7VIPfv6L+Pp8ZtUG9OCvD/++4yg0EfiM28Cb7euwa5OY9L3GYViaoYIfBTVcN1zL0qpaajVd6GUmw9WWQEFrLKilJ2HmrNJkt+zJEmSJEmSZDxqziYoZedFu88jf3cI80bbWwnx+gKKopCuVVccWnUj6ORBvMYNQAv5N7nLWCoPP87rjP+jV+zoMIfwwNBoc/i+8I917rjuZf9hZ2fHoEED2L17DydOnEjye/3SyJYNn6lOnbqwfPlK7t27Rfbs2T95frMCw/B+FvMvS/qsDqy9PQ6hafw97A9ubz5FxSG/ULxNjffnaGFheE8fSfC546T9rS32TdqiKAri1TG04y6g6VEr/YGSoZpB36MkSZIkSZKU+gn/a2hHf4NQb5RyC1Bz/AZAwKFd+MybiHnOvGQYPhVdGvv31zw8cJndPReRqXRe6i3ujom5KfDpe9aPCQoKInfu/BQokJ9Dhw4kuHf15062bPjCPHjwgGXLVtCpU4d4JXwA39UrFuOYuaUZ7dwaIITg2Pg/ub35FGV71I2e8AUF4jWmL8HnT5CuQ18cmrZDURS0+8vQDtYF83SotY/IhE+SJEmSJOkrpdh/g1r7KDiUQpz8He3KKITQsK36I86DxhP+7CGew7sS6eP1/ppcNYpTY0JrXpy5w55ei9FHRD3/186tAeaWZtHG15noaOfW4JNxWFtbM2zYYI4cOcrBg4cM+yY/czLp+wy5uY3BxMSEoUMHx+v8Z3e92PvHKTLlcsIpiz2KEvVrSd95LtRoUpazs3dw9Y9DFGtTnTLdfnp/XaT/azxHdCP07nXS9xlFmh9+RmiRaOf7Ic72gAxVUWsdQrHLk1xvVZIkSZIkSfoMKBZOqNX+QsndBnFjCtqxZoiIAKzLVCTDyBno/f14MbQL4c8fv78mf4NyVBnZlMeHrnJg8Ao0vUaNJmXpO8+F9FkdgKhFCk2vkS5DzPZisenYsQNZs2Zl2LARfAk7Gg1Fbu/8zNy+fZvChYvRp08vpk6d/MnzQ4LC6F5lEv4+AbifGEL6LA7RXr+0bD8nJm2i0C/fUXVsi/fL4BFeL3g5ug96/9c4DxqPVfFyiDA/tBOtwOsQSoEeKMXHoagxG7pLkiRJkiRJXychBOLuAsTFQWBXALXKnyg2OQh7dA+vMf0QkRFkGD4Vi3yF319zYdEeTk3bSuEmlfjerXm0bZkhgaF0rTKJd6+DcD85FKdMaT8Zw+LFS+jYsQs7d27nxx9/SI63mSrJ7Z1fkNGjx2FpacmgQQM+ea4Qghk91vDkthdDl7WN0quL5QAAIdBJREFUkfDd2HCME5M2kadOSb4f7fL+L1jY4/t4Du2CFhhARrfZUQnf2ztoe78H7+NRe7VLTpQJnyRJkiRJkhSNoiio+buifr8Vgj3R9lZBeJ/APGdeMo1fgGptw8tRvQi+fOb9NaU61qFkx9rcWH+Mk1M2R1uhs7SxYJRHR0KDwxnTcjGREfpYZo2uTZvW5MyZE1dXN7na939ype8zcvPmTYoUKc7Agf2ZOHH8J8/fvvgIs3qvo82IerQc/CN3dpzh9PRtBLz0wyKtNaFvgsheqTA/zuuCzswEgJCbl3k1fhCKpRUZR07HLGtOxMsDaMdbgWqKWnkditO3yf1WJUmSJEmSpM+ceHcP7civEPQYpcws1NytifR/jdeYvoQ/f0z6niOwqRhVS0IIwRG3tVxfe5Q8P5Tk1ZXHBLz0wzajA+X7NuBFqMq4Nsv4uVs1uk7+9ZNzL1u2nHbtOrJ9+xbq1aub3G81VZArfV8IN7exWFtb079/30+ee/v8Y+YN+JOytQrjMrAOd3ac4dBwDwI8/UBAqH8QoJC7Tsn3CV/Q+RN4je6Dzt6BzOMXYJolB9qd+WiHG4F1VtQ6x2TCJ0mSJEmSJMWLYpcXtfYhSF8JcaYr2sXB6NKkJeOYuVjkK4z3jFG83b056lxFocrIpmQomYv7uy++v2cN8PTj0HAPMltoNOpSlU3zDnJ406cbsLds2YLcuXPL1b7/k0nfZ+LatWts2PAnvXr1wNHR8aPnvvUNxM1lEekypmHI0t9RVZXT07cRGRq9OTtCcG7uTgACDu/h1cQhmGbNSaax89E5OCDO9URcGACZf0St+TeKdbbkenuSJEmSJEnSF0gxs0f9fgtKvi6I23PQjvyCaqqRYcQMrEpV4PXiafhvWI4QAkVVCXwZs09fZGg4p6dvo9P4xhQql4upXf/g6R2vWGb7l6mpKSNHDuPSpcts27Y9ud7eZ0MmfZ8JN7ex2NnZ0bdv74+ep9drjG+7DH/vAFw9OmLnYA1AwMvYG1oGvPTj7V8b8Jk9BovCxck0eg6qhYZ2qD7i/jKUQv1RK61FMbUx9FuSJEmSJEmSvgKKaoJaeipKmdngdRBtXzWU8Bc4DxyPzfd18F+3hNfLZiE0jUCv2JuzB7z0w9TMhJF/tMfMwpRRLosI+aCx+4eaN29G3rx5cXV1Q9O05Hhrnw2Z9H0GLl++zKZNm+nduycODg4fPfePCTs5//ctuk39jfwl/+3hZ+VoF8vZgiI53/J62SysylUmw7ApKOFP0fZWAd+zKN8uRS3uhqLI/0wkSZIkSZKkpFHztkOtth1CvaPuN1+fxKn7MOzq/sa7nX/iM2csdhnTxnqtbcaoe2CnzPYMX9GOZ3e8mN7d46NbN01MTHB1Hc7Vq9fYvHlLcrylz4a8m/8MjBo1hjRp0tCnT6+Pnnd23w1WT9xNzeblqNu24vvjb5/5EBkW8cHZgm+yvCKb5VNsq9fFuf8YFN/DaPuqQWQwao29qDmbJsO7kSRJkiRJkr5WinMV1NqHwSI92sF6iIcrSPd7T+ybdyTwyF4qfPMaM0uTGNcV+PnfuhIlqxagzYh6HPzzPNsWHfnofE2bNqFAgQKMGjXmq17ti/lPVEpVLly4wLZt2xk9ehRp06aN87xXT18zvt1ychTKRO9Z//Y3CfJ+y7bfZ6EoCpWb5cbk0m7M1TA0oaJTNdI0dMG+RWfE3XmIS0MhbRHUyn+iWGdJkfcnSZIkSZIkfV0U29yotQ6inWiNONsD3t4ibeMJ6GzT4LtoKtWLOxLm/xZzNZwwzZwHAdm4uvIQuWuWxDF/ZgCa9a/NjTMPWTBoI/lLZqdgmZyxzqXT6XB1HU6zZi3488+NNGnyW0q+1VRDtmxI5erVa8iJEyd5/Pg+dnaxbdGE8LAIetecxrO7r5h/bDBZ8zoDEOIfyJaW0wl48Zof+5UhcucyRFjYvxfqdDh1H4S1xS7EgxWQtQHqt4tRTKxT4J1JkiRJkiRJXzOhRSIuDUXcmQcZa6B+twrfVcsI2LUx+omm5tx8nQPvUEcae/Qnbfb0ALzzC6JLxQloeg33E0NJ4xh7DQq9Xk+xYqXQNI1r1y6h032ZvaZly4bP1NmzZ/nrr5307983zoQPYMHgjdy58ISBC1u9T/jCA0P5q+Nc3j7x5qf5XRDHNkdP+ABVF4buRifEgxUohQeiVlwtEz5JkiRJkiQpRSiqCWqpyShl54JX1GNG4df2xjwxIozC2V6jRejZ9vvM98Ve7BysGbm6A/7eAUxotxy9Pvbtm/+s9t26dYv16zck51tKteRKXyr244/1OHv2HI8e3cPW1jbWc/5ef5bxbZfzS4/qdJn4CwCRYRHs6DAXz/P3+GF2R3LVKM7Dxt9hldUP+yIv0VlFoA8xQVFBNdWjVlqKmqNJSr41SZIkSZIkSXpP/K+9+46Polr/OP55djeVQAi9hyJiQakiiFcFFURpFi4gIgKKBa8dwXIVkCJ2+VkQxKtiBCsSFQsCioJSVARR4ALSQXqTQNr5/bGrN0DAEMhOsnzfr9e+mD0zu3nm2cNknpyZs7/PIPvrbmTt3snu5aVJSN4ePGfdG8X2nyuyd21pEgalMPHap0koX5IrUu4mrlTw/PjDsV/zzG1v0uOBy7j2/ty/iD07O5v69Ruxf386ixb9RCAQeXe5aaSvCPr222/55JNP6dfv7sMWfCt/Wc9Tt6ZQ95xa3PDI5QBkZWTx2R1jWDd7CRcN70HNi+qTsXEt8dV2ULrRGgLFMjCDQHwmvphMdq2uqYJPRERERDxl5c8LTvBCgMRTN/3vnLVYBqUbrSHh5H2Uq5tM21G3sGvdVlKv/z/S96QB0LbXuVx89dm8Pmwyc6f8kuv7+3w+Bg16mKVLlzJ+/ITw7VghoaKvkHr44cGULVuWvn1vznX93t37GNhtNHEJsfz79esJRPlx2dlMu/91fpu2gPP+3Zk6Hc4mfdVy1t9/C0l11+MLHDiqawbFT9oVjt0RERERETkiK14Li0sgNB/hX3wBR2Kt5eydP4fKTU6mzcg+bF2ylo9ueoHMfemYGXc8ezXVT6vEsN6v8Pua3L+fumPHDtSvX4/Bg4eSmZkZhj0qPFT0FUIzZ85kypQv6N+/HwkJh96Q6pzjyb5vsG7ZJh58rTdlKpbEOceMoW+zJHU2Z9/enjOvacG+xQtZ/2Bf8Pnwx6fn+rN8WVsKendERERERPLEl517weaPS2fjsH7smTWd6hecwUWP9WT9vGV8cvsYsjKyiI2P5uGUG8hMz+KR7mPISD+0qDMzBg16mGXLljFu3BsFvSuFioq+QmjgwEcoV64cN998Y67rJ744nS/f+55eD3eg/nknAzBn5IcsfONL6ve8iMY3t2Hvj7PZMOgO/CVKUmnQo5gvJvcfFq+vZhARERGRQuIw56YWW4GYk05j01MPsWtKKidfdhYXPNyVVV8uZOqAV3HZ2VStXZ57X7qWX+euZNR97+X6Pu3ataVRo4YMHfroCTXap6KvkJk1axZffDGVe++9h/j4+EPWL5q9glH3vUezS8+g810XAzD/1S+Y+8JkTr3yHJr3v5I/Zk5l4/B7iapUlYr33Y3vh87gssCiDnwzfxxWb1A4dktERERE5G9ZvUHgjzt0RdYeKvTtTFz9Jmx5cQQ7JqZQt+t5NL2rI0s/mstXgyfgnOMfHRpw1b8u5INRXzLtnbmHvr8ZDz30IMuXLycl5c0w7FHhoKKvkEhJGU/16rVo3vx8fD4fJUsmHbLNjs27eaT7y5SrkkT/0T3w+Xz88t4svhn+LrVaN6DFI9ewe0oqm54eSOzJp1Px1k7Ytx0hfQe+iz7Fmr4E8VUBg/iqWJPn8dXQJC4iIiIiUjj4anTGmjx/wDkr9YdBXAX4+nLKd65HsX9czLZxL7D19RdoeEMrGvS+mJ/Hz2D2M6kA3PDI5dRtVosn+6awavGGQ35Gu3ZtSU5O5vrrb8QsiurVa5GSMj7MexpekTdXaRGUkjKePn1uYu/evUBwStnbbrud2NgYunXrCkBWVjbDe/+HHVt2M3JqP4onFWP55z8y/cFxVG1+Kq2e6MWuSSlse2MU8Y3OoewVteC7rlDiZHznv4clJGNlm4GKPBEREREpxHw1Oh9yzupqdSf7m2twc2+mzAV34Y/vwM4PUsj+YzfN7rqb/bvTmDfqE2IS42nQ62IefP16bjpnGAO7jeaFr/oTlxD713u9+eYENmzY8NflnatWraZPn5sA/jr3jjT6nr5CoHr1WqxatfqQ9uTkaqxcuRyA14Z+xOvDPuau57pxWc9zWTPrVz7s8zxlT69K+7G3sef9V9j5wZsk/ONCSp/7B/x3FFRqja/5q1jU4b/YXURERESkKHDZGbh5d+OWjYUqbdm59lx2vDuBYue0pMytDzBlwDiWffI9LYd057ROzflh+mL6tx9Ji6sac98rPbHQtKB5Ofcuio70PX0a6SsEVq9ec8T2uVN+YdzwyVx89dlcel1zNs5fweS+o0iqUZ62o25m12vPsHvqR5RocylJtebAf7/A6tyKNRiG+fzh3BURERERkQJhvig461lIPAX3Q38Sk1bjv7Y7W19PIXvvHi4cPJj0PfuY/tAbRCfE0rBNI677dzteGZRK3XNq0f6G84G/P/eORLqnrxCoVq3qYdt/X7ONYb1fofpplbjj2avZunQ9H/Z5jvgyJWj30s3sevkxdk/9iKSr2pFU8X34/UusyXP4Go1QwSciIiIiEcXM8NW5Bd/578Lu30hwT1KuTyfSFsxj0/B7aD28KxXq1+Tzfq+w+ptf6HpPa85uXZcX7n2Xxd+vBI587h2pVPQVAr169TykLT4+nsGDBvNI9zFkpmfxcMoN7N+yk9TezxKIiaL9qD7sHjOEP76dTtnul1Ai5kXY9zu+lqn4Tjr0/UREREREIoVVao2v1VTwxRC38xEq3tSK/cuXsHnYXbQZ0YVStSoy+dZR/D7/Nwa8fB2lKpRgULcx7Ny6h6FDhxwyS350dDRDhw7xaG8Knoq+QmDOnLkkJBSjatUqmBnJydUYPXoUuxYG+HXuSu55sTtJJWKY1PNZsjKyaPd8L/aMeYS0hd9ToXdz4vc/BdFJ+FpNx8qf7/XuiIiIiIgUOCt5Or7WX0KpBkRvepTKfc4k4/d1bBl+F5c+2oli5RL56MbnSd+0nYfeuIHtv+/i0etfpWvXzowePYrk5GoAREVFUaZMabp0+ae3O1SAVPR5bN68eXz88WQGDOjP6tW/kZ2dzsqVy6kUU5uJL37JlX1bcnaLOqT2Hknatt20faob+/4zlP0rllC5V21idj4PZZvhaz0dK1Hb690REREREQkbiy2Hr+XHWPWuBDaNpcq1iWSn7WT7E/24bEhHAnHRpPYeSYUy8dzy2FXM+XwR45/4jG7durJy5XKcy+CNN15j/foNvPPOu17vToHR7J0ea9/+cr75ZiYrVy6jRIngLJurl2zklvMepWbdyox4/xY+uuE5tvy6hraPdcaljiR7zxYqd47Ft2sGdlIvrPFTwRtbRUREREROQM453C9P4H4aiCtejw2Ti5P1hxHf8z5SB6QSVSyWK968h/+7byLT353HiNTbaNjiFCD4dWlnnNEAgIULf8TnK5rjYkeavVNFn4d++OEHGjU6m8GDB9Ls5IsZ+/AkNq3Zhj/gJzo2wMvfPcB3g1JYN2cpbQa2xf/ZKHy+PVS8bCeWthRrMByr0/ev6WdFRERERE5kbvUHZH97PS4qiU0za7J/QxYxne/kw4GfU7xSKdq8dCt3d3yezWu3E5cQy7aNOyhbpRSnty7Pv0fezVtvvck//9nJ693IFxV9hVTHjlfy1VczeP2pd3ix30T2p6X/tS462k/n8yuz59dVXHxPC2K+fpXoUmmUO3cVlr0XX/PXsMqXeBi9iIiIiEjh47b9SPZXnXDpu9j2y5ns+TWTQLs+fPzYLErXqUzmWWcydvBHB7wmJi6KjXELSU/czoIFPxTJ0b4jFX1Fb28ixPz585k0KZU77riNN0dMOaDgA0fdYpns+XUVF9zQkOjpY4ivtodyTRdiUbH4Wk1VwSciIiIikgsr1QBf6xlYidqUrjObpCbZZE56gdY31mXzL2tY8con+Dhw4Gt/WgZVqMeiRYt4//2JHkVecDTS55ErrujEtGnTWblyGVdVuY/KsY7TEyHOD5kOonyQ4d9Jh7rrSWqURomqi6B0E3znTcDiynsdvoiIiIhIoeYy/yB71vWwNpW0naew6YsYMht35PNXV7A9HWJ8wXPvtCxYtBPW7TPWVvma6Ogo5s//vsiN9mmkr5BZuHAhEyd+wO23/4uSJUtySqU4GiRBfADMggVfpcTttDt1JeXO20qJqouw5E74LvpEBZ+IiIiISB5YoBi+f6Rgp91DXOJiKl62jej571Gx7BaSot1f597xAWiQBKdWjufBB+9j4cKfmTQp1evwjyuN9HmgS5dufPzxZFatWk7JkiV55vR/kZywhTrlfycuKoOMLB+x8ekkNV1NQrld2Bn/xur214QtIiIiIiL5kL3iDdycW8nKKM7GKeVZv6E0CTH7iIvKJC0jiiW/l2ezVeH6WSM45ZS6JCaWYN682UXq/PtII32BcAdzoluyZAlvv/0O/fv3o1SpUox/8jOSE7bQrOlCSp+5Hn98BllpAcwHBLKx5q/jS77S67BFRERERIosX81rcAk1YEYXyrf4L8WWbyUheXvw3HtvFKUWVOLb7yAQCHD//f3p3bsPn376GW3aRMY8GhrpC7MePXryzjvvsXLlMtYv3kG/y55hbI8ZVGyyEl/gf5+Fc7Dl58pUGL7Uw2hFRERERCKH272C/W81JBCXQc5BvOxMY/3s6vx69mhadmlM7dqnUrlyJWbOnFFkRvt0T18hsWLFClJSxnPjjTfgz4phSI+x1KlRkvL11h5Q8EHw+uKkmps8ilREREREJPJY8ZqYcxxcx/kCjgr11/LsnRNYs3gTAwb049tvv2P69C89ifN4U9EXRiNGPI7f7+euO+9kyHVjiUrby5mB3fjjM3Ld/nDtIiIiIiKSP/74zMO0Z5CcFMWga0bzzys7U7FiRYYMGRbm6AqGir4wWbNmDf/5z2v07t2TT8fMZfl3S2lRGc5KXorLyn3I2AXKhjlKEREREZHIdrhz7My0KM4qtof967bwf3e8Tb9+dzF9+pfMnDkzzBEefyr6wuTxx5/EOUerszrywbOfcWlNP81PWsApreZhAXAHzanjLAZ/kxEeRSsiIiIiEpn8TUbgLOaANufAF8jmgoY/cUlyBgsm/0j57DqULVuWIUOGexTp8aOiLww2btzImDFj6XrFNbzx4Me0SXa0rDeP2q3nE5Xkx98yFV+z0RBfFTCIr4qv6Yv4anT2OnQRERERkYjiq9EZX9MXDzj3tnqD8SWUo1rLRVzUbA7tamTwztAP6dXpJj799DPmzp3rddjHRF/ZEAZPPvk0melZRC2rwLmJ62l/3lwqNV0BcZXwX/whllgHA1CRJyIiIiJS4Hw1Oh9y7u1qXUPWF5dTqflPXFo8DfdpU6Z9tpuyJSswdOijfPDBex5Fe+w00lfAtmzZwosvvsSlp1xN3T2r6dphBpWbL8cl1sffdhaWWMfrEEVERERETngWVx7/pdNw5dtS5sx1XN5pOhcWX0fLCpczadKHLFiwwOsQ801FXwF75pmRFN9XkQstjZ7XTaXMGevJLteewGVTsdgyXocnIiIiIiIh5o/F33IC7qQ7KVFjG516TKV9ybXUTWjG0KGPeh1evoW96DOzS8xsiZktM7MBuayPMbO3Qutnm1n1cMd4vOzYsYMxI8cxsMHp9O4zheLJ28mueTeBC9/E/DF//wYiIiIiIhJWZkagyRBo+AKxZfZyRc/pDD4rga/en83ixYu9Di9fwlr0mZkfeB5oA5wGdDWz0w7arDew3Tl3EvA0UGSnsHz68ZE81qAOPfpMJTopDVd/FFFNB2MHfxukiIiIiIgUKv5TemAXfIRFG5dd+zXj2lZl8ANFszQJ90hfE2CZc26Fcy4dmAB0OGibDsBroeV3gQutCFZJu3btot6iT7m610zwgZ3/MYHTunsdloiIiIiI5JG/yvkEOswmI70YLa6cy22JP7F4UdEb7Qt30VcZWJPj+dpQW67bOOcygZ1A6bBEdxwtfqgV7Tv9RNrueKI6zCZQ7TyvQxIRERERkaPkT6pN3LWL2Lm5HGddtATfuMvJyszwOqyjUmQncjGzPmY2z8zmbd682etwDhHtS2fr6tLEdluAv8zJXocjIiIiIiL55IstRclbF/Pbz1WIiUknOyvb65COSri/p28dUDXH8yqhtty2WWtmASAR2HrwGznnRgOjARo3buwKJNpjcOYT88jcv4/ouHivQxERERERkWPkj4oheeBCfDj80UVrUsZwj/TNBWqbWQ0ziwa6AKkHbZMK9AgtXwVMc84VuqLu7/h8PhV8IiIiIiIRJCo6usgVfBDmkT7nXKaZ3Qp8BviBV5xzi8xsMDDPOZcKjAXGmdkyYBvBwlBERERERETyIdyXd+KcmwxMPqjtoRzL+4BO4Y5LREREREQkEhXZiVxERERERETk76noExERERERiWAq+kRERERERCKYij4REREREZEIpqJPREREREQkgqnoExERERERiWAq+kRERERERCKYij4REREREZEIpqJPREREREQkgqnoExERERERiWAq+kRERERERCKYij4REREREZEIpqJPREREREQkgqnoExERERERiWAq+kRERERERCKYOee8juGYmdlmYJXXceSiDLDF6yBOUMq9d5R77yj33lL+vaPce0e5945y753Cmvtk51zZ3FZERNFXWJnZPOdcY6/jOBEp995R7r2j3HtL+feOcu8d5d47yr13imLudXmniIiIiIhIBFPRJyIiIiIiEsFU9BWs0V4HcAJT7r2j3HtHufeW8u8d5d47yr13lHvvFLnc654+ERERERGRCKaRPhERERERkQimoi+fzOwSM1tiZsvMbEAu62PM7K3Q+tlmVj3HuvtC7UvMrHVYA48Aecj9XWb2i5ktMLOpZpacY12Wmc0PPVLDG3nRl4fcX2dmm3Pk+Poc63qY2X9Djx7hjbzoy0Pun86R96VmtiPHOvX7Y2Bmr5jZJjP7+TDrzcxGhj6bBWbWMMc69ftjkIfcdwvlfKGZzTKzejnWrQy1zzezeeGLOjLkIfcXmNnOHMeWh3KsO+LxSo4sD7nvlyPvP4eO8aVC69Tvj4GZVTWz6aHzyEVmdnsu2xTNY75zTo+jfAB+YDlQE4gGfgJOO2ibW4BRoeUuwFuh5dNC28cANULv4/d6n4rKI4+5bwHEh5Zv/jP3oed7vN6HovrIY+6vA57L5bWlgBWhf5NCy0le71NReeQl9wdt/y/glRzP1e+PLf/nAQ2Bnw+z/lLgE8CApsDsULv6fcHn/pw/cwq0+TP3oecrgTJe70NRfeQh9xcAH+XSflTHKz2OPvcHbdsOmJbjufr9seW+ItAwtFwcWJrLuU6RPOZrpC9/mgDLnHMrnHPpwASgw0HbdABeCy2/C1xoZhZqn+Cc2++c+w1YFno/yZu/zb1zbrpzbm/o6XdAlTDHGKny0u8PpzUwxTm3zTm3HZgCXFJAcUaio819V2B8WCI7ATjnZgDbjrBJB+B1F/QdUNLMKqJ+f8z+LvfOuVmh3IKO98dVHvr94RzL7wrhqHOv4/1x5Jzb4Jz7IbS8G/gVqHzQZkXymK+iL38qA2tyPF/LoR3ir22cc5nATqB0Hl8rh3e0+etN8K8xf4o1s3lm9p2ZdSyA+CJZXnN/Zehyh3fNrOpRvlZyl+f8hS5nrgFMy9Gsfl+wDvf5qN+H18HHewd8bmbfm1kfj2KKdM3M7Ccz+8TMTg+1qd+HiZnFEywq3svRrH5/nFjw1qwGwOyDVhXJY37A6wBECoqZXQM0Bs7P0ZzsnFtnZjWBaWa20Dm33JsII9KHwHjn3H4zu5HgaHdLj2M60XQB3nXOZeVoU7+XiGZmLQgWfefmaD431O/LAVPMbHFoBEWOjx8IHlv2mNmlwAdAbW9DOuG0A2Y653KOCqrfHwdmlkCwmL7DObfL63iOB4305c86oGqO51VCbbluY2YBIBHYmsfXyuHlKX9mdhHwANDeObf/z3bn3LrQvyuALwn+BUfy5m9z75zbmiPfLwON8vpaOaKjyV8XDrrUR/2+wB3u81G/DwMzO5Pg8aaDc27rn+05+v0mYCK6leK4cs7tcs7tCS1PBqLMrAzq9+F0pOO9+n0+mVkUwYIvxTn3fi6bFMljvoq+/JkL1DazGmYWTfA/3cEz4qUCf87acxXBm2xdqL2LBWf3rEHwr2JzwhR3JPjb3JtZA+AlggXfphztSWYWE1ouAzQHfglb5EVfXnJfMcfT9gSvhQf4DGgV+gySgFahNsmbvBxzMLNTCN48/m2ONvX7gpcKXBua0a0psNM5twH1+wJnZtWA94HuzrmlOdqLmVnxP5cJ5j7XmRAlf8ysQmiuAsysCcFzyq3k8Xglx8bMEgleyTQpR5v6/TEK9emxwK/OuacOs1mRPObr8s58cM5lmtmtBD9IP8FZ8haZ2WBgnnMulWCHGWdmywjejNsl9NpFZvY2wZOuTKDvQZdhyRHkMfePAwnAO6HfR6udc+2BU4GXzCyb4C+nR51zOvnNozzm/jYza0+wb28jOJsnzrltZvYIwZMBgMEHXY4iR5DH3EPwODMh9AemP6nfHyMzG09wpsIyZrYWeBiIAnDOjQImE5zNbRmwF+gZWqd+f4zykPuHCN4v/0LoeJ/pnGsMlAcmhtoCwJvOuU/DvgNFWB5yfxVws5llAmlAl9CxJ9fjlQe7UGTlIfcAlwOfO+f+yPFS9ftj1xzoDiw0s/mhtvuBalC0j/l24LmBiIiIiIiIRBJd3ikiIiIiIhLBVPSJiIiIiIhEMBV9IiIiIiIiEUxFn4iIiIiISART0SciIiIiIhLBVPSJiIiIiIhEMBV9IiIiIiIiEUxFn4iIiIiISART0SciInKUzKyYmS02szlmFpWjvZWZZZtZXy/jExERycmcc17HICIiUuSYWQPgO+Bp59wAMysP/ATMds518DY6ERGR/1HRJyIikk9mdifwBNAauAc4A6jnnNviaWAiIiI5qOgTERHJJzMz4GOgJRANXOycm+ptVCIiIgfSPX0iIiL55IJ/OR0HxAA/qeATEZHCSEWfiIhIPplZBeBZ4Aegnpnd7nFIIiIih1DRJyIikg+hSztfA/YDFwHPACPM7Ewv4xIRETmY7ukTERHJBzO7G3gMaOmc+8rMognO5hkDNHbOpXkaoIiISIhG+kRERI6SmTUEhgHDnXNfATjn0oGuQHXgKe+iExEROZBG+kRERERERCKYRvpEREREREQimIo+ERERERGRCKaiT0REREREJIKp6BMREREREYlgKvpEREREREQimIo+ERERERGRCKaiT0REREREJIKp6BMREREREYlgKvpEREREREQi2P8Dw/Hjke4YRlcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_nodes = mesh[\"rod\"].nodes  # numerical gridpoints\n",
    "xx = np.linspace(0, 2, 101)  # fine mesh to plot exact solution\n",
    "plot_times = np.linspace(0, 1, 5)  # times at which to plot\n",
    "\n",
    "plt.figure(figsize=(15, 8))\n",
    "cmap = plt.get_cmap(\"inferno\")\n",
    "for i, t in enumerate(plot_times):\n",
    "    color = cmap(float(i) / len(plot_times))\n",
    "    plt.plot(\n",
    "        x_nodes,\n",
    "        T_out(t, x=x_nodes),\n",
    "        \"o\",\n",
    "        color=color,\n",
    "        label=\"Numerical\" if i == 0 else \"\",\n",
    "    )\n",
    "    plt.plot(\n",
    "        xx,\n",
    "        T_exact(xx, t),\n",
    "        \"-\",\n",
    "        color=color,\n",
    "        label=f\"Exact (t={plot_times[i]})\",\n",
    "    )\n",
    "plt.xlabel(\"x\", fontsize=16)\n",
    "plt.ylabel(\"T\", fontsize=16)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "The relevant papers for this notebook are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
      "[2] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
      "[3] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n",
      "[4] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pybamm.print_citations()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
